517,623 results on '"Hannah, A."'
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52. Humanity's Last Exam
- Author
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Phan, Long, Gatti, Alice, Han, Ziwen, Li, Nathaniel, Hu, Josephina, Zhang, Hugh, Zhang, Chen Bo Calvin, Shaaban, Mohamed, Ling, John, Shi, Sean, Choi, Michael, Agrawal, Anish, Chopra, Arnav, Khoja, Adam, Kim, Ryan, Ren, Richard, Hausenloy, Jason, Zhang, Oliver, Mazeika, Mantas, Nguyen, Tung, Anderson, Daron, Shah, Imad Ali, Doroshenko, Mikhail, Stokes, Alun Cennyth, Mahmood, Mobeen, Lee, Jaeho, Pokutnyi, Oleksandr, Iskra, Oleg, Wang, Jessica P., Gerbicz, Robert, Levin, John-Clark, Popov, Serguei, Feng, Fiona, Feng, Steven Y., Zhao, Haoran, Yu, Michael, Gangal, Varun, Zou, Chelsea, Wang, Zihan, Kazakov, Mstyslav, Galgon, Geoff, Schmitt, Johannes, Sanchez, Alvaro, Lee, Yongki, Yeadon, Will, Sauers, Scott, Roth, Marc, Agu, Chidozie, Riis, Søren, Giska, Fabian, Utpala, Saiteja, Cheatom, Antrell, Giboney, Zachary, Goshu, Gashaw M., Crowson, Sarah-Jane, Naiya, Mohinder Maheshbhai, Burns, Noah, Finke, Lennart, Cheng, Zerui, Park, Hyunwoo, Fournier-Facio, Francesco, Zampese, Jennifer, Wydallis, John B., Hoerr, Ryan G., Nandor, Mark, Gehrunger, Tim, Cai, Jiaqi, McCarty, Ben, Nam, Jungbae, Taylor, Edwin, Jin, Jun, Loume, Gautier Abou, Cao, Hangrui, Garretson, Alexis C, Sileo, Damien, Ren, Qiuyu, Cojoc, Doru, Arkhipov, Pavel, Qazi, Usman, Bacho, Aras, Li, Lianghui, Motwani, Sumeet, de Witt, Christian Schroeder, Kopylov, Alexei, Veith, Johannes, Singer, Eric, Rissone, Paolo, Jin, Jaehyeok, Shi, Jack Wei Lun, Willcocks, Chris G., Prabhu, Ameya, Tang, Longke, Zhou, Kevin, Santos, Emily de Oliveira, Maksimov, Andrey Pupasov, Vendrow, Edward, Zenitani, Kengo, Robinson, Joshua, Mikov, Aleksandar, Guillod, Julien, Li, Yuqi, Pageler, Ben, Vendrow, Joshua, Kuchkin, Vladyslav, Marion, Pierre, Efremov, Denis, Lynch, Jayson, Liang, Kaiqu, Gritsevskiy, Andrew, Martinez, Dakotah, Crispino, Nick, Zvonkine, Dimitri, Fraga, Natanael Wildner, Soori, Saeed, Press, Ori, Tang, Henry, Salazar, Julian, Green, Sean R., Brüssel, Lina, Twayana, Moon, Dieuleveut, Aymeric, Rogers, T. Ryan, Zhang, Wenjin, Finocchio, Ross, Li, Bikun, Yang, Jinzhou, Rao, Arun, Loiseau, Gabriel, Kalinin, Mikhail, Lukas, Marco, Manolescu, Ciprian, Stambaugh, Nate, Mishra, Subrata, Kamdoum, Ariel Ghislain Kemogne, Hogg, Tad, Jin, Alvin, Bosio, Carlo, Sun, Gongbo, Coppola, Brian P, Heidinger, Haline, Sayous, Rafael, Ivanov, Stefan, Cavanagh, Joseph M, Shen, Jiawei, Imperial, Joseph Marvin, Schwaller, Philippe, Senthilkuma, Shaipranesh, Bran, Andres M, Algaba, Andres, Verbeken, Brecht, Houte, Kelsey Van den, Van Der Sypt, Lynn, Noever, David, Schut, Lisa, Sucholutsky, Ilia, Zheltonozhskii, Evgenii, Yuan, Qiaochu, Lim, Derek, Stanley, Richard, Sivarajan, Shankar, Yang, Tong, Maar, John, Wykowski, Julian, Oller, Martí, Sandlin, Jennifer, Sahu, Anmol, Ardito, Cesare Giulio, Hu, Yuzheng, Dias, Felipe Meneguitti, Kreiman, Tobias, Rawal, Kaivalya, Vilchis, Tobias Garcia, Zu, Yuexuan, Lackner, Martin, Koppel, James, Nguyen, Jeremy, Antonenko, Daniil S., Chern, Steffi, Zhao, Bingchen, Arsene, Pierrot, Ivanov, Sergey, Poświata, Rafał, Wang, Chenguang, Li, Daofeng, Crisostomi, Donato, Dehghan, Ali, Achilleos, Andrea, Ambay, John Arnold, Myklebust, Benjamin, Sen, Archan, Perrella, David, Kaparov, Nurdin, Inlow, Mark H, Zang, Allen, Ramakrishnan, Kalyan, Orel, Daniil, Poritski, Vladislav, Ben-David, Shalev, Berger, Zachary, Whitfill, Parker, Foster, Michael, Munro, Daniel, Ho, Linh, Hava, Dan Bar, Kuchkin, Aleksey, Lauff, Robert, Holmes, David, Sommerhage, Frank, Zhang, Anji, Moat, Richard, Schneider, Keith, Pyda, Daniel, Kazibwe, Zakayo, Singh, Mukhwinder, Clarke, Don, Kim, Dae Hyun, Fish, Sara, Elser, Veit, Vilchis, Victor Efren Guadarrama, Klose, Immo, Demian, Christoph, Anantheswaran, Ujjwala, Zweiger, Adam, Albani, Guglielmo, Li, Jeffery, Daans, Nicolas, Radionov, Maksim, Rozhoň, Václav, Ginis, Vincent, Ma, Ziqiao, Stump, Christian, Platnick, Jacob, Nevirkovets, Volodymyr, Basler, Luke, Piccardo, Marco, Cohen, Niv, Singh, Virendra, Tkadlec, Josef, Rosu, Paul, Goldfarb, Alan, Padlewski, Piotr, Barzowski, Stanislaw, Montgomery, Kyle, Menezes, Aline, Patel, Arkil, Wang, Zixuan, Tucker-Foltz, Jamie, Stade, Jack, Grabb, Declan, Goertzen, Tom, Kazemi, Fereshteh, Milbauer, Jeremiah, Shukla, Abhishek, Elgnainy, Hossam, Labrador, Yan Carlos Leyva, He, Hao, Zhang, Ling, Givré, Alan, Wolff, Hew, Demir, Gözdenur, Aziz, Muhammad Fayez, Kaddar, Younesse, Ängquist, Ivar, Chen, Yanxu, Thornley, Elliott, Zhang, Robin, Pan, Jiayi, Terpin, Antonio, Muennighoff, Niklas, Schoelkopf, Hailey, Zheng, Eric, Carmi, Avishy, Shah, Jainam, Brown, Ethan D. L., Zhu, Kelin, Bartolo, Max, Wheeler, Richard, Ho, Andrew, Barkan, Shaul, Wang, Jiaqi, Stehberger, Martin, Kretov, Egor, Bradshaw, Peter, Heimonen, JP, Sridhar, Kaustubh, Hossain, Zaki, Akov, Ido, Makarychev, Yury, Tam, Joanna, Hoang, Hieu, Cunningham, David M., Goryachev, Vladimir, Patramanis, Demosthenes, Krause, Michael, Redenti, Andrew, Aldous, David, Lai, Jesyin, Coleman, Shannon, Xu, Jiangnan, Lee, Sangwon, Magoulas, Ilias, Zhao, Sandy, Tang, Ning, Cohen, Michael K., Carroll, Micah, Paradise, Orr, Kirchner, Jan Hendrik, Steinerberger, Stefan, Ovchynnikov, Maksym, Matos, Jason O., Shenoy, Adithya, Wang, Michael, Nie, Yuzhou, Giordano, Paolo, Petersen, Philipp, Sztyber-Betley, Anna, Faraboschi, Paolo, Riblet, Robin, Crozier, Jonathan, Halasyamani, Shiv, Pinto, Antonella, Verma, Shreyas, Joshi, Prashant, Meril, Eli, Yong, Zheng-Xin, Tee, Allison, Andréoletti, Jérémy, Weller, Orion, Singhal, Raghav, Zhang, Gang, Ivanov, Alexander, Khoury, Seri, Gustafsson, Nils, Mostaghimi, Hamid, Thaman, Kunvar, Chen, Qijia, Khánh, Tran Quoc, Loader, Jacob, Cavalleri, Stefano, Szlyk, Hannah, Brown, Zachary, Narayan, Himanshu, Roberts, Jonathan, Alley, William, Sun, Kunyang, Stendall, Ryan, Lamparth, Max, Reuel, Anka, Wang, Ting, Xu, Hanmeng, Hernández-Cámara, Pablo, Martin, Freddie, Preu, Thomas, Korbak, Tomek, Abramovitch, Marcus, Williamson, Dominic, Bosio, Ida, Chen, Ziye, Bálint, Biró, Lo, Eve J. 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B., Shamseldeen, Samir, Karim, Loukmane, Liakhovitskaia, Anna, Resman, Nate, Farina, Nicholas, Gonzalez, Juan Carlos, Maayan, Gabe, Hoback, Sarah, Pena, Rodrigo De Oliveira, Sherman, Glen, Kelley, Elizabeth, Mariji, Hodjat, Pouriamanesh, Rasoul, Wu, Wentao, Mendoza, Sandra, Alarab, Ismail, Cole, Joshua, Ferreira, Danyelle, Johnson, Bryan, Safdari, Mohammad, Dai, Liangti, Arthornthurasuk, Siriphan, Pronin, Alexey, Fan, Jing, Ramirez-Trinidad, Angel, Cartwright, Ashley, Pottmaier, Daphiny, Taheri, Omid, Outevsky, David, Stepanic, Stanley, Perry, Samuel, Askew, Luke, Rodríguez, Raúl Adrián Huerta, Minissi, Ali M. R., Ali, Sam, Lorena, Ricardo, Iyer, Krishnamurthy, Fasiludeen, Arshad Anil, Salauddin, Sk Md, Islam, Murat, Gonzalez, Juan, Ducey, Josh, Somrak, Maja, Mavroudis, Vasilios, Vergo, Eric, Qin, Juehang, Borbás, Benjámin, Chu, Eric, Lindsey, Jack, Radhakrishnan, Anil, Jallon, Antoine, McInnis, I. M. J., Kumar, Pawan, Goswami, Laxman Prasad, Bugas, Daniel, Heydari, Nasser, Jeanplong, Ferenc, Apronti, Archimedes, Galal, Abdallah, Ze-An, Ng, Singh, Ankit, Xavier, Joan of Arc, Agarwal, Kanu Priya, Berkani, Mohammed, Junior, Benedito Alves de Oliveira, Malishev, Dmitry, Remy, Nicolas, Hartman, Taylor D., Tarver, Tim, Mensah, Stephen, Gimenez, Javier, Montecillo, Roselynn Grace, Campbell, Russell, Sharma, Asankhaya, Meer, Khalida, Alapont, Xavier, Patil, Deepakkumar, Maheshwari, Rajat, Dendane, Abdelkader, Shukla, Priti, Bogdanov, Sergei, Möller, Sören, Siddiqi, Muhammad Rehan, Saxena, Prajvi, Gupta, Himanshu, Enyekwe, Innocent, P V, Ragavendran, EL-Wasif, Zienab, Maksapetyan, Aleksandr, Rossbach, Vivien, Harjadi, Chris, Bahaloohoreh, Mohsen, Bian, Song, Lai, John, Uro, Justine Leon, Bateman, Greg, Sayed, Mohamed, Menshawy, Ahmed, Duclosel, Darling, Jain, Yashaswini, Aaron, Ashley, Tiryakioglu, Murat, Siddh, Sheeshram, Krenek, Keith, Hoover, Alex, McGowan, Joseph, Patwardhan, Tejal, Yue, Summer, Wang, Alexandr, and Hendrycks, Dan
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Computer Science - Machine Learning ,Computer Science - Artificial Intelligence ,Computer Science - Computation and Language - Abstract
Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achieve over 90\% accuracy on popular benchmarks like MMLU, limiting informed measurement of state-of-the-art LLM capabilities. In response, we introduce Humanity's Last Exam (HLE), a multi-modal benchmark at the frontier of human knowledge, designed to be the final closed-ended academic benchmark of its kind with broad subject coverage. HLE consists of 2,700 questions across dozens of subjects, including mathematics, humanities, and the natural sciences. HLE is developed globally by subject-matter experts and consists of multiple-choice and short-answer questions suitable for automated grading. Each question has a known solution that is unambiguous and easily verifiable, but cannot be quickly answered via internet retrieval. State-of-the-art LLMs demonstrate low accuracy and calibration on HLE, highlighting a significant gap between current LLM capabilities and the expert human frontier on closed-ended academic questions. To inform research and policymaking upon a clear understanding of model capabilities, we publicly release HLE at https://lastexam.ai., Comment: 27 pages, 6 figures
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- 2025
53. Improved algorithms and novel applications of the FrankWolfe.jl library
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Besançon, Mathieu, Designolle, Sébastien, Halbey, Jannis, Hendrych, Deborah, Kuzinowicz, Dominik, Pokutta, Sebastian, Troppens, Hannah, Herrmannsdoerfer, Daniel Viladrich, and Wirth, Elias
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Mathematics - Optimization and Control ,Computer Science - Mathematical Software - Abstract
Frank-Wolfe (FW) algorithms have emerged as an essential class of methods for constrained optimization, especially on large-scale problems. In this paper, we summarize the algorithmic design choices and progress made in the last years of the development of FrankWolfe.jl, a Julia package gathering high-performance implementations of state-of-the-art FW variants. We review key use cases of the library in the recent literature, which match its original dual purpose: first, becoming the de-facto toolbox for practitioners applying FW methods to their problem, and second, offering a modular ecosystem to algorithm designers who experiment with their own variants and implementations of algorithmic blocks. Finally, we demonstrate the performance of several FW variants on important problem classes in several experiments, which we curated in a separate repository for continuous benchmarking.
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- 2025
54. JWST COMPASS: NIRSpec/G395H Transmission Observations of the Super-Earth TOI-776b
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Alderson, Lili, Moran, Sarah E., Wallack, Nicole L., Batalha, Natasha E., Wogan, Nicholas F., Dattilo, Anne, Wakeford, Hannah R., Redai, Jea Adam, Alam, Munazza K., Aguichine, Artyom, Batalha, Natalie M., Gagnebin, Anna, Gao, Peter, Kirk, James, López-Morales, Mercedes, Meech, Annabella, Teske, Johanna, and Wolfgang, Angie
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Astrophysics - Earth and Planetary Astrophysics - Abstract
We present two transit observations of the $\sim$520K, 1.85R$_\oplus$, 4.0M$_\oplus$ super-Earth TOI-776b with JWST NIRSpec/G395H, resulting in a 2.8-5.2$\mu$m transmission spectrum. Producing reductions using the ExoTiC-JEDI and Eureka! pipelines, we obtain a median transit depth precision of 34ppm for both visits and both reductions in spectroscopic channels 30 pixels wide ($\sim$0.02$\mu$m). We find that our independent reductions produce consistent transmission spectra, however, each visit shows differing overall structure. For both reductions, a flat line is preferred for Visit 1 while a flat line with an offset between the NRS1 and NRS2 detectors is preferred for Visit 2; however, we are able to correct for this offset during our modeling analysis following methods outlined in previous literature. Using picaso forward models, we can rule out metallicities up to at least 100$\times$ solar with an opaque pressure of 10$^{-3}$ bar to $\geq$3$\sigma$ in all cases, however, the exact lower limit varies between the visits, with Visit 1 ruling out $\lesssim$100$\times$ solar while the lower limits for Visit 2 extend beyond $\sim$350$\times$ solar. Our results add to the growing list of super-Earth atmospheric constraints by JWST, which provide critical insight into the diversity and challenges of characterizing terrestrial planets., Comment: 17 pages, 7 figures, 4 tables. Accepted for publication in AJ. arXiv admin note: text overlap with arXiv:2404.00093
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- 2025
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55. Auto-Evaluation: A Critical Measure in Driving Improvements in Quality and Safety of AI-Generated Lesson Resources
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Clark, Hannah-Beth, Dowland, Margaux, Benton, Laura, Budai, Reka, Keskin, Ibrahim Kaan, Searle, Emma, Gregory, Matthew, Hodierne, Mark, Gayne, William, and Roberts, John
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Computer Science - Computers and Society ,Computer Science - Artificial Intelligence - Abstract
As a publicly funded body in the UK, Oak National Academy is in a unique position to innovate within this field as we have a comprehensive curriculum of approximately 13,000 open education resources (OER) for all National Curriculum subjects, designed and quality-assured by expert, human teachers. This has provided the corpus of content needed for building a high-quality AI-powered lesson planning tool, Aila, that is free to use and, therefore, accessible to all teachers across the country. Furthermore, using our evidence-informed curriculum principles, we have codified and exemplified each component of lesson design. To assess the quality of lessons produced by Aila at scale, we have developed an AI-powered auto-evaluation agent,facilitating informed improvements to enhance output quality. Through comparisons between human and auto-evaluations, we have begun to refine this agent further to increase its accuracy, measured by its alignment with an expert human evaluator. In this paper we present this iterative evaluation process through an illustrative case study focused on one quality benchmark - the level of challenge within multiple-choice quizzes. We also explore the contribution that this may make to similar projects and the wider sector., Comment: 27 pages, Part of MIT Open Learning AI and Open Education Initiative Series, published Jan 2025 https://aiopeneducation.pubpub.org/pub/i36sncz8/release/3?readingCollection=06969c6d
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- 2025
56. Constructing reducibly geometrically finite subgroups of the mapping class group
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Aougab, Tarik, Bray, Harrison, Dowdall, Spencer, Hoganson, Hannah, Maloni, Sara, and Whitfield, Brandis
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Mathematics - Geometric Topology ,Mathematics - Group Theory ,20F65, 20F67, 57K20, 57M60 - Abstract
In this article, we consider qualified notions of geometric finiteness in mapping class groups called parabolically geometrically finite (PGF) and reducibly geometrically finite (RGF). We examine several constructions of subgroups and determine when they produce a PGF or RGF subgroup. These results provide a variety of new examples of PGF and RGF subgroups. Firstly, we consider the right-angled Artin subgroups constructed by Koberda and Clay--Leininger--Mangahas, which are generated by high powers of given elements of the mapping class group. We give conditions on the supports of these elements that imply the resulting right-angled Artin subgroup is RGF. Secondly, we prove combination theorems which provide conditions for when a collection of reducible subgroups, or sufficiently deep finite-index subgroups thereof, generate an RGF subgroup., Comment: 34 pages, 6 figures
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- 2025
57. Resolution Dependence of Cloud-Wind Simulations
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Leary, Hannah J., Schneider, Evan, and Richie, Helena M.
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Astrophysics - Astrophysics of Galaxies - Abstract
Using a set of wind-tunnel simulations, we investigate the effect that numerical resolution has on cloud evolution and acceleration. We also consider the role of wind speed in both the subsonic and supersonic regimes, using adiabatic simulations with wind speeds of 100 km/s and 1000 km/s, respectively. For each setup, we explore five numerical resolutions. We find that the fate of the cloud is significantly affected by the resolution, but the trend is surprisingly non-monotonic. In the subsonic case, we find that a resolution of 16 cells per cloud radius is a turning point in the cloud evolution. In the supersonic case the trend is more monotonic, consistent with the difference in timescales for which ram pressure acceleration dominates over mixing in the early acceleration.
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- 2025
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58. A Bayesian Modelling Framework with Model Comparison for Epidemics with Super-Spreading
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Craddock, Hannah, Spencer, Simon EF, and Didelot, Xavier
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Quantitative Biology - Quantitative Methods - Abstract
The transmission dynamics of an epidemic are rarely homogeneous. Super-spreading events and super-spreading individuals are two types of heterogeneous transmissibility. Inference of super-spreading is commonly carried out on secondary case data, the expected distribution of which is known as the offspring distribution. However, this data is seldom available. Here we introduce a multi-model framework fit to incidence time-series, data that is much more readily available. The framework consists of five discrete-time, stochastic, branching-process models of epidemics spread through a susceptible population. The framework includes a baseline model of homogeneous transmission, a unimodal and a bimodal model for super-spreading events, as well as a unimodal and a bimodal model for super-spreading individuals. Bayesian statistics is used to infer model parameters using Markov Chain Monte-Carlo. Model comparison is conducted by computing Bayes factors, with importance sampling used to estimate the marginal likelihood of each model. This estimator is selected for its consistency and lower variance compared to alternatives. Application to simulated data from each model identifies the correct model for the majority of simulations and accurately infers the true parameters, such as the basic reproduction number. We also apply our methods to incidence data from the 2003 SARS outbreak and the Covid-19 pandemic. Model selection consistently identifies the same model and mechanism for a given disease, even when using different time series. Our estimates are consistent with previous studies based on secondary case data. Quantifying the contribution of super-spreading to disease transmission has important implications for infectious disease management and control. Our modelling framework is disease-agnostic and implemented as an R package, with potential to be a valuable tool for public health.
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- 2025
59. Leveraging Large Language Models to Enhance Machine Learning Interpretability and Predictive Performance: A Case Study on Emergency Department Returns for Mental Health Patients
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Ahmed, Abdulaziz, Saleem, Mohammad, Alzeen, Mohammed, Birur, Badari, Fargason, Rachel E, Burk, Bradley G, Harkins, Hannah Rose, Alhassan, Ahmed, and Al-Garadi, Mohammed Ali
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Computer Science - Machine Learning ,Computer Science - Artificial Intelligence ,Computer Science - Computers and Society - Abstract
Importance: Emergency department (ED) returns for mental health conditions pose a major healthcare burden, with 24-27% of patients returning within 30 days. Traditional machine learning models for predicting these returns often lack interpretability for clinical use. Objective: To assess whether integrating large language models (LLMs) with machine learning improves predictive accuracy and clinical interpretability of ED mental health return risk models. Methods: This retrospective cohort study analyzed 42,464 ED visits for 27,904 unique mental health patients at an academic medical center in the Deep South from January 2018 to December 2022. Main Outcomes and Measures: Two primary outcomes were evaluated: (1) 30-day ED return prediction accuracy and (2) model interpretability using a novel LLM-enhanced framework integrating SHAP (SHapley Additive exPlanations) values with clinical knowledge. Results: For chief complaint classification, LLaMA 3 (8B) with 10-shot learning outperformed traditional models (accuracy: 0.882, F1-score: 0.86). In SDoH classification, LLM-based models achieved 0.95 accuracy and 0.96 F1-score, with Alcohol, Tobacco, and Substance Abuse performing best (F1: 0.96-0.89), while Exercise and Home Environment showed lower performance (F1: 0.70-0.67). The LLM-based interpretability framework achieved 99% accuracy in translating model predictions into clinically relevant explanations. LLM-extracted features improved XGBoost AUC from 0.74 to 0.76 and AUC-PR from 0.58 to 0.61. Conclusions and Relevance: Integrating LLMs with machine learning models yielded modest but consistent accuracy gains while significantly enhancing interpretability through automated, clinically relevant explanations. This approach provides a framework for translating predictive analytics into actionable clinical insights.
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- 2025
60. ORCAst: Operational High-Resolution Current Forecasts
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Garcia, Pierre, Larroche, Inès, Pesnec, Amélie, Bull, Hannah, Archambault, Théo, Moschos, Evangelos, Stegner, Alexandre, Charantonis, Anastase, and Béréziat, Dominique
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Computer Science - Computer Vision and Pattern Recognition ,Physics - Atmospheric and Oceanic Physics - Abstract
We present ORCAst, a multi-stage, multi-arm network for Operational high-Resolution Current forecAsts over one week. Producing real-time nowcasts and forecasts of ocean surface currents is a challenging problem due to indirect or incomplete information from satellite remote sensing data. Entirely trained on real satellite data and in situ measurements from drifters, our model learns to forecast global ocean surface currents using various sources of ground truth observations in a multi-stage learning procedure. Our multi-arm encoder-decoder model architecture allows us to first predict sea surface height and geostrophic currents from larger quantities of nadir and SWOT altimetry data, before learning to predict ocean surface currents from much more sparse in situ measurements from drifters. Training our model on specific regions improves performance. Our model achieves stronger nowcast and forecast performance in predicting ocean surface currents than various state-of-the-art methods.
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- 2025
61. How Does the Spatial Distribution of Pre-training Data Affect Geospatial Foundation Models?
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Purohit, Mirali, Muhawenayo, Gedeon, Rolf, Esther, and Kerner, Hannah
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Computer Science - Machine Learning ,Computer Science - Computer Vision and Pattern Recognition - Abstract
Foundation models have made rapid advances in many domains including Earth observation, where Geospatial Foundation Models (GFMs) can help address global challenges such as climate change, agriculture, and disaster response. Previous work on GFMs focused on tailoring model architecture and pre-text tasks, and did not investigate the impact of pre-training data selection on model performance. However, recent works from other domains show that the pre-training data distribution is an important factor influencing the performance of the foundation models. With this motivation, our research explores how the geographic distribution of pre-training data affects the performance of GFMs. We evaluated several pre-training data distributions by sampling different compositions from a global data pool. Our experiments with two GFMs on downstream tasks indicate that balanced and globally representative data compositions often outperform region-specific sampling, highlighting the importance of diversity and global coverage in pre-training data. Our results suggest that the most appropriate data sampling technique may depend on the specific GFM architecture. These findings will support the development of robust GFMs by incorporating quality pre-training data distributions, ultimately improving machine learning solutions for Earth observation., Comment: Accepted at Good Data for Generative AI @ AAAI 2025
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- 2025
62. Connection-Coordination Rapport (CCR) Scale: A Dual-Factor Scale to Measure Human-Robot Rapport
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Lin, Ting-Han, Dinner, Hannah, Leung, Tsz Long, Mutlu, Bilge, Trafton, J. Gregory, and Sebo, Sarah
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Computer Science - Robotics ,Computer Science - Human-Computer Interaction - Abstract
Robots, particularly in service and companionship roles, must develop positive relationships with people they interact with regularly to be successful. These positive human-robot relationships can be characterized as establishing "rapport," which indicates mutual understanding and interpersonal connection that form the groundwork for successful long-term human-robot interaction. However, the human-robot interaction research literature lacks scale instruments to assess human-robot rapport in a variety of situations. In this work, we developed the 18-item Connection-Coordination Rapport (CCR) Scale to measure human-robot rapport. We first ran Study 1 (N = 288) where online participants rated videos of human-robot interactions using a set of candidate items. Our Study 1 results showed the discovery of two factors in our scale, which we named "Connection" and "Coordination." We then evaluated this scale by running Study 2 (N = 201) where online participants rated a new set of human-robot interaction videos with our scale and an existing rapport scale from virtual agents research for comparison. We also validated our scale by replicating a prior in-person human-robot interaction study, Study 3 (N = 44), and found that rapport is rated significantly greater when participants interacted with a responsive robot (responsive condition) as opposed to an unresponsive robot (unresponsive condition). Results from these studies demonstrate high reliability and validity for the CCR scale, which can be used to measure rapport in both first-person and third-person perspectives. We encourage the adoption of this scale in future studies to measure rapport in a variety of human-robot interactions., Comment: 8 pages, 5 figures
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- 2025
63. The JCMT Gould Belt Survey: First results from the Corona Australis molecular cloud and evidence of variable dust emissivity indices in the Coronet region
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Pattle, Kate, Bresnahan, David, Ward-Thompson, Derek, Kirk, Helen, Kirk, Jason M., Berry, David S., Broekhoven-Fiene, Hannah, Hatchell, Jenny, Jenness, Tim, Johnstone, Doug, Mottram, J. C., Duarte-Cabral, Ana, Di Francesco, James, Hogerheijde, M. R., Bastien, Pierre, Butner, Harold, Chen, Michael, Chrysostomou, Antonio, Coudé, Simon, Currie, Malcolm J., Davis, C. J., Drabek-Maunder, Emily, Fich, M., Fiege, J., Friberg, Per, Friesen, Rachel, Fuller, Gary A., Graves, Sarah, Greaves, Jane, Holland, Wayne, Joncas, G., Knee, L. B. G., Mairs, Steve, Marsh, Ken, Matthews, Brenda C., Moriarty-Schieven, Gerald, Mowat, C., Rawlings, Jonathan, Retter, B., Richer, John, Robertson, D., Rosolowsky, E., Sadavoy, Sarah, Thomas, H., Tothill, N., Viti, Serena, White, G. J., Wouterloot, J., Yates, J., and Zhu, M.
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Astrophysics - Astrophysics of Galaxies - Abstract
We present 450$\mu$m and 850$\mu$m James Clerk Maxwell Telescope (JCMT) observations of the Corona Australis (CrA) molecular cloud taken as part of the JCMT Gould Belt Legacy Survey (GBLS). We present a catalogue of 39 starless and protostellar sources, for which we determine source temperatures and masses using SCUBA-2 450$\mu$m/850$\mu$m flux density ratios for sources with reliable 450$\mu$m detections, and compare these to values determined using temperatures measured by the Herschel Gould Belt Survey (HGBS). In keeping with previous studies, we find that SCUBA-2 preferentially detects high-volume-density starless cores, which are most likely to be prestellar (gravitationally bound). We do not observe any anti-correlation between temperature and volume density in the starless cores in our sample. Finally, we combine our SCUBA-2 and Herschel data to perform SED fitting from 160-850$\mu$m across the central Coronet region, thereby measuring dust temperature $T$, dust emissivity index $\beta$ and column density $N({\rm H}_2)$ across the Coronet. We find that $\beta$ varies across the Coronet, particularly measuring $\beta = 1.55 \pm 0.35$ in the colder starless SMM-6 clump to the north of the B star R CrA. This relatively low value of $\beta$ is suggestive of the presence of large dust grains in SMM-6, even when considering the effects of $T-\beta$ fitting degeneracy and $^{12}$CO contamination of SCUBA-2 850$\mu$m data on the measured $\beta$ values., Comment: 27 pages, 18 figures, 4 tables. Accepted for publication in MNRAS
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- 2025
64. Efficient auto-labeling of large-scale poultry datasets (ALPD) using an ensemble model with self- and active-learning approaches
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Bist, Ramesh Bahadur, Chai, Lilong, Weimer, Shawna, Atungulua, Hannah, Pennicott, Chantel, Yang, Xiao, Subedi, Sachin, Pallerla, Chaitanya, Tian, Yang, and Wang, Dongyi
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Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Artificial Intelligence - Abstract
The rapid growth of artificial intelligence in poultry farming has highlighted the challenge of efficiently labeling large, diverse datasets. Manual annotation is time-consuming and costly, making it impractical for modern systems that continuously generate data. This study addresses this challenge by exploring semi-supervised auto-labeling methods, integrating self and active learning approaches to develop an efficient, label-scarce framework for auto-labeling large poultry datasets (ALPD). For this study, video data were collected from broilers and laying hens housed. Various machine learning models, including zero-shot models and supervised models, were utilized for broilers and hens detection. The results showed that YOLOv8s-World and YOLOv9s performed better when compared performance metrics for broiler and hen detection under supervised learning, while among the semi-supervised model, YOLOv8s-ALPD achieved the highest precision (96.1%) and recall (99%) with an RMSE of 1.87. The hybrid YOLO-World model, incorporating the optimal YOLOv8s backbone with zero-shot models, demonstrated the highest overall performance. It achieved a precision of 99.2%, recall of 99.4%, and an F1 score of 98.7% for detection. In addition, the semi-supervised models with minimal human intervention (active learning) reduced annotation time by over 80% compared to full manual labeling. Moreover, integrating zero-shot models with the best models enhanced broiler and hen detection, achieving comparable results to supervised models while significantly increasing speed. In conclusion, integrating semi-supervised auto-labeling and zero-shot models significantly improves detection accuracy. It reduces manual annotation efforts, offering a promising solution to optimize AI-driven systems in poultry farming, advancing precision livestock management, and promoting more sustainable practices.
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- 2025
65. PISCO: Self-Supervised k-Space Regularization for Improved Neural Implicit k-Space Representations of Dynamic MRI
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Spieker, Veronika, Eichhorn, Hannah, Huang, Wenqi, Stelter, Jonathan K., Catalan, Tabita, Braren, Rickmer F., Rueckert, Daniel, Costabal, Francisco Sahli, Hammernik, Kerstin, Karampinos, Dimitrios C., Prieto, Claudia, and Schnabel, Julia A.
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Electrical Engineering and Systems Science - Image and Video Processing ,Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Machine Learning ,Electrical Engineering and Systems Science - Signal Processing ,Physics - Medical Physics - Abstract
Neural implicit k-space representations (NIK) have shown promising results for dynamic magnetic resonance imaging (MRI) at high temporal resolutions. Yet, reducing acquisition time, and thereby available training data, results in severe performance drops due to overfitting. To address this, we introduce a novel self-supervised k-space loss function $\mathcal{L}_\mathrm{PISCO}$, applicable for regularization of NIK-based reconstructions. The proposed loss function is based on the concept of parallel imaging-inspired self-consistency (PISCO), enforcing a consistent global k-space neighborhood relationship without requiring additional data. Quantitative and qualitative evaluations on static and dynamic MR reconstructions show that integrating PISCO significantly improves NIK representations. Particularly for high acceleration factors (R$\geq$54), NIK with PISCO achieves superior spatio-temporal reconstruction quality compared to state-of-the-art methods. Furthermore, an extensive analysis of the loss assumptions and stability shows PISCO's potential as versatile self-supervised k-space loss function for further applications and architectures. Code is available at: https://github.com/compai-lab/2025-pisco-spieker
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- 2025
66. Automating Explanation Need Management in App Reviews: A Case Study from the Navigation App Industry
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Obaidi, Martin, Voß, Nicolas, Droste, Jakob, Deters, Hannah, Herrmann, Marc, Fischbach, Jannik, and Schneider, Kurt
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Computer Science - Software Engineering - Abstract
Providing explanations in response to user reviews is a time-consuming and repetitive task for companies, as many reviews present similar issues requiring nearly identical responses. To improve efficiency, this paper proposes a semi-automated approach to managing explanation needs in user reviews. The approach leverages taxonomy categories to classify reviews and assign them to relevant internal teams or sources for responses. 2,366 app reviews from the Google Play Store and Apple App Store were scraped and analyzed using a word and phrase filtering system to detect explanation needs. The detected needs were categorized and assigned to specific internal teams at the company Graphmasters GmbH, using a hierarchical assignment strategy that prioritizes the most relevant teams. Additionally, external sources, such as existing support articles and past review responses, were integrated to provide comprehensive explanations. The system was evaluated through interviews and surveys with the Graphmasters support team, which consists of four employees. The results showed that the hierarchical assignment method improved the accuracy of team assignments, with correct teams being identified in 79.2% of cases. However, challenges in interrater agreement and the need for new responses in certain cases, particularly for Apple App Store reviews, were noted. Future work will focus on refining the taxonomy and enhancing the automation process to reduce manual intervention further., Comment: This paper has been accepted at the Software Engineering in Practice (SEIP) track of the 47th International Conference on Software Engineering (ICSE 2025)
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- 2025
67. Impacts of EPA's Finalized Power Plant Greenhouse Gas Standards
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Bistline, John, Bergman, Aaron, Blanford, Geoffrey, Brown, Maxwell, Burtraw, Dallas, Domeshek, Maya, Fawcett, Allen, Hamilton, Anne, Iyer, Gokul, Jenkins, Jesse, King, Ben, Kolus, Hannah, Levin, Amanda, Luo, Qian, Rennert, Kevin, Robertson, Molly, Roy, Nicholas, Russell, Ethan, Shawhan, Daniel, Steinberg, Daniel, van Brummen, Anna, Van Horn, Grace, Venkatesh, Aranya, Weyant, John, Wiser, Ryan, and Zhao, Alicia
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Physics - Physics and Society - Abstract
The Inflation Reduction Act subsidizes the deployment of clean electricity, hydrogen production, and carbon capture and storage, which could enable additional actions by other federal, state, and local policymakers to reduce emissions. Power plant rules finalized by the Environmental Protection Agency (EPA) in 2024 are one such example of complementary policies. The rules establish emissions intensity standards, not technology mandates, meaning power plant owners can choose from a range of technologies and control options provided that emissions standards are met. This flexibility makes electricity systems modeling important to understand the potential effects of these regulations. We report below a multi-model analysis of the EPA power plant rules that can provide timely information, including for other countries and states, on emissions impacts, policy design for electricity decarbonization, power sector investments and retirements, cost impacts, and load growth. We also discuss related technical, political, and legal uncertainties.
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- 2025
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68. A Starter Kit for Diversity-Oriented Communities for Undergraduates: Near-Peer Mentorship Programs
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Griffith, Emily J., Lee, Gloria, Corbo, Joel C., Huckabee, Gabriela, Shamloo, Hannah Inés, Quan, Gina, Charles, Noah, Gutmann, Brianne, Jones-Hall, Gabrielle, Nakib, Mayisha Zeb, Pollard, Benjamin, Romanelli, Marisa, Shafer, Devyn, Smith, Megan Marshall, and Turpen, Chandra
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Physics - Physics Education - Abstract
This mentoring resource is a guide to establishing and running near-peer mentorship programs. It is based on the working knowledge and best practices developed by the Access Network, a collection of nine student-led communities at universities across the country working towards a vision of a more diverse, equitable, inclusive, and accessible STEM environment. Many of these communities, also referred to as sites, include a near-peer mentoring program that is developed to best support their local context. The format of these programs vary, ranging from structured classes with peer mentoring groups to student clubs supporting 1-on-1 relationships. To further support program participants as both students and as whole people, sites often run additional events such as lecture series, workshops, and social activities guided tailored to each student community's needs. Through this process, student leaders have generated and honed best practices for all aspects of running their sites. This guide is an attempt to synthesize those efforts, offering practical advice for student leaders setting up near-peer mentorship programs in their own departments. It has been written through the lens of undergraduate near-peer mentorship programs, although our framework could easily be extended to other demographics (e.g. high schoolers, graduate students, etc.). Our experience is with STEM mentorship specifically, though these practices can extend to any discipline. In this document, we outline best practices for designing, running, and sustaining near-peer mentorship programs. We provide template resources to assist with this work, and lesson plans to run mentor and mentee training sessions. We hope you find this guide useful in designing, implementing, and re-evaluating community oriented near-peer mentoring programs.
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- 2025
69. Behavioural Analytics: Mathematics of the Mind
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Lane, Richard, State-Davey, Hannah, Taylor, Claire, Holmes, Wendy, Boon, Rachel, and Round, Mark
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Computer Science - Computers and Society ,Computer Science - Machine Learning - Abstract
Behavioural analytics provides insights into individual and crowd behaviour, enabling analysis of what previously happened and predictions for how people may be likely to act in the future. In defence and security, this analysis allows organisations to achieve tactical and strategic advantage through influence campaigns, a key counterpart to physical activities. Before action can be taken, online and real-world behaviour must be analysed to determine the level of threat. Huge data volumes mean that automated processes are required to attain an accurate understanding of risk. We describe the mathematical basis of technologies to analyse quotes in multiple languages. These include a Bayesian network to understand behavioural factors, state estimation algorithms for time series analysis, and machine learning algorithms for classification. We present results from studies of quotes in English, French, and Arabic, from anti-violence campaigners, politicians, extremists, and terrorists. The algorithms correctly identify extreme statements; and analysis at individual, group, and population levels detects both trends over time and sharp changes attributed to major geopolitical events. Group analysis shows that additional population characteristics can be determined, such as polarisation over particular issues and large-scale shifts in attitude. Finally, MP voting behaviour and statements from publicly-available records are analysed to determine the level of correlation between what people say and what they do., Comment: 19 pages, 14 figures, presented at 7th IMA Conference on Mathematics in Defence and Security, London, UK, 7 September 2023 (conference page at https://ima.org.uk/20850/7th-ima-defence/)
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- 2025
70. Curating life in vacant spaces: Community action research and reversing the process of academic knowledge-making
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Dombroski, Kelly, Shiels, Rachael, and Watkinson, Hannah
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- 2025
71. Improving Independent Purchasing Skills in Postsecondary Students with Disabilities
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Addison L. Welch, Christopher H. Skinner, Hannah L. Collins, Mitchell R. Chutna, Cate Smith, David Cihak, and Brian Wilhoit
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An experimental design was used to evaluate the effects of a classroom-based purchasing skills intervention designed to teach postsecondary students with intellectual and developmental disabilities the one-dollar-more purchasing strategy. The experimental group students (n = 12) completed the intervention first,and the control group participants (n = 9) did not receive the intervention until experimental procedures ceased. The intervention included eight intervention sessions. Each session included researcher-led, small-group instruction designed to enhance acquisition of the one-dollar-more strategy, followed by response card learning trials designed to enhance skill development. The response card trials contained different price points that reflected common items available for purchase in community retail settings. Results showed significant improvement in the experimental group's ability to provide the correct amount of dollar bill across 20 purchase prices up to $39 within 10 s. No significant changes were observed in the control group. However, individual analysis showed that some students who received the intervention did not show socially valid improvements. Also, group data suggest that most of the improvement may have occurred after three intervention sessions. Although our group data showed increases in purchasing skill acquisition (accuracy), fluency (within 10 s), and generalization (different values), discussion focuses on future research designed to enhance the efficacy of this intervention across all students and to assess for generalization to natural settings.
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- 2025
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72. Behavioral Skills Training to Teach Self-Instruction of Video Activity Schedules for Vocational Skills
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Amy D. Spriggs, Sally B. Shepley, Mark D. Samudre, Hannah E. Keene, Kai O'Neill, and Shealynn Hall
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This study evaluated the effects of behavior skills training (BST) as a means to acquire and generalize self-instruction behaviors using video activity schedules (VidAS) loaded on an iPod Touch for four U.S. high school students with intellectual disabilities. Behavior skills training procedures were implemented in two different vocational training environments and evaluated using a multiple probe across participants design. Following the mastery criterion in the training environments, the generalization of self-instruction behaviors to two different vocational training environments was assessed. All participants acquired self-instruction behaviors with BST instruction. However, only three of four participants independently generalized responding to two additional vocational environments. Results and directions for future research regarding training loosely and using multiple exemplars to program for generalization are discussed.
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- 2025
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73. Contesting the Framing of Digital Risk: An Analysis of Australian Children's Experiences
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Hannah Soong, Sue Nichols, Karen Dooley, and Michelle M. Neumann
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Children's social lives are mediated increasingly by digital tools, thereby making new modes of home-school-world relationships possible. In this context, children's digital out-of-school social practices continue to be associated with risk. Children are construed as an "at risk" and vulnerable population by adults who frame children's everyday digital social life with ambivalence. This framing is used to justify imposition of constraints in the present, with children's digital engagement a key site of regulation. The study we report here problematises research that separates risk from children's digital social participation. To challenge dominant views of children being vulnerable to risk, we use Actor Network Theory to provide a nuanced exposition of concepts of "digital risk" and agentic self, relevant to children's digital social lives. From the findings of the present study, involving 62 10-11-year-old children in 3 Australian schools, we argue that "digital risk" framing of children's social lives underplays children's agency and voice in digital environments. Additionally, what is missing in the framing of digital risk is recognition that children utilise digital tools to ameliorate non-digital risk. In other words, risk is not only digital; and there is more to digital participation than risk.
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- 2025
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74. Intercultural Mentoring: Enhancing Cultural Competence through Intergroup Contact and Experiential Learning
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Bolanle Oyindamola Adebayo and Hannah M. Sunderman
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Purpose: To maximize the benefits of intercultural mentoring relationships, which are increasing in today's diverse higher education environment, the current article conceptualizes the connection between intercultural mentoring and cultural competence among mentors and mentees as a learning process. Design/methodology/approach: The conceptual paper discusses the nuanced aspects of intercultural mentoring. Ultimately, the current article presents a framework for a bidirectional relationship between intercultural mentoring and cultural competence through experiential learning theory and intergroup contact theory, resulting in implications for practitioners and actionable research directions. Findings: The article highlights the interplay and interdependence of cultural competence and intercultural mentoring through experiential learning and intergroup contact theory. Cultural competence influences the quality of intercultural mentoring relationships. Conversely, intercultural mentoring relationships can develop cultural competence in mentors and mentees through experiential learning, producing positive intergroup contact behaviors. Findings suggest the need for active learning and unlearning among mentors and mentees in intercultural mentoring relationships to maximize developmental outcomes (e.g. cultural competence). Originality/value: The proposed framework emphasizes that (1) the possession of cultural competence is a critical success factor for intercultural mentoring relationships, (2) the development of cultural competence is an outcome of successful intercultural mentoring relationships, and (3) intercultural mentoring relationships should be regarded as experiential learning platforms that can produce positive intercultural traits such as cultural competence.
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- 2025
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75. Measuring Preservice Teacher Knowledge and Attitudes to Support Youth Experiencing Homelessness
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Stacey Havlik, Peter Wiens, Arash Ghafoori, Melissa Jacobowitz, Kelly-Jo Sheback, and Hannah Hudson
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While many teachers are unaware that students in their classes are experiencing homelessness, others may not know how to support students who are identified as lacking consistent housing (Wright et al., 2019). Thus, there is a critical need to better assess, understand, and enhance teachers' knowledge and attitudes toward homelessness. Therefore, the purpose of the present study is threefold--researchers sought to (1) design and test a survey to measure the knowledge and attitudes of preservice teachers working with youth experiencing homelessness; (2) measure the knowledge and attitudes of preservice teachers related to homelessness; and (3) measure the effectiveness of a virtual training program designed to enhance preservice teachers' knowledge and attitudes toward homelessness using the finalized survey. Results of the data analysis were promising--indicating that the Knowledge and Attitudes on Homelessness Survey for Educators (KAHS) instrument is a reliable tool in measuring knowledge and attitudes related to homelessness and the online training program has a positive impact on teachers' knowledge and attitudes.
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- 2025
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76. Integration of Individuals with Lived Experience to Improve Recruitment within Criminal Justice Research: 'Experience as the Best Teacher'
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Imani Randolph, Raven Simonds, Dalia Sharps, Jamala Wallace, Hannah Joseph, René Ropac, and Tiffany Bergin
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Research has documented numerous barriers to recruitment of marginalized individuals, including historic exploitation, distrust, and accessibility. Such challenges are commonly observed among racial minorities, unhoused individuals, and individuals with lower socioeconomic statuses. Public health researchers have led investigations on ways to engage hard-to-reach populations, but the nuances of recruiting such individuals within criminal justice research remain less explored -- a crucial oversight given the prevalence of these populations within the criminal legal system. While literature has identified barriers unique to these distinct identities, it has not fully explored how intersectional identities may complicate these barriers. To bridge these critical gaps, the current paper addresses the challenges of recruiting individuals with multiple identities within the same study. We discuss how the proposed techniques for recruitment of these populations translate into criminal justice contexts, with particular attention to the ways individuals with lived experience enhance study design, recruitment, and overall integrity.
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- 2025
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77. How Do I Want to Be Read? Embodied Autobiographical and Discoursal Selves in Preservice English Teachers' Feedback to High School Writers
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Andrea R. Olinger, Alison Heron-Hruby, James S. Chisholm, Braydon L. Dungan, Hannah M. Conn, and Julia P. Nitishin
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Research on writing pedagogy highlights effective feedback as dialogic and student-centered. However, few studies have examined how preservice English teachers (PSETs) craft their feedback based on how they want their students to read them. Drawing on sociocultural approaches to writing, we examine the co-construction of these "discoursal selves" by studying PSETs' talk and embodied actions during focus-group conversations about their feedback to student writers. Data included videorecorded focus groups, transcripts, and written feedback. By navigating their life histories ("autobiographical selves") and the identities they sought to convey in their feedback ("discoursal selves"), PSETs struggled to contend with their internalized grammarians while projecting a peer identity that was encouraging even when they offered critique. We argue that reflection with PSETs on how they want to be read can empower them to make intentional instructional choices.
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- 2025
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78. Consensus Building Using the Delphi Method in Educational Research: A Case Study with Educational Professionals
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Emily Oxley, Hannah M. Nash, and Anna R. Weighall
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The study explores the Delphi consensus technique's application in educational research, focusing on identifying barriers and facilitators to educational attainment for children speaking English as an Additional Language (EAL) from the perspective of their teachers. It discusses the methodology's advantages and considerations within an educational context, reflecting on the findings and their implications for future research. Through iterative rounds of surveys among educators, key challenges like linguistic barriers, lack of pedagogical knowledge, and insufficient support were highlighted. The paper advocates for enhanced teacher training and resource allocation to address these barriers effectively. This research contributes to the understanding of consensus-building methods in education and underscores the need for strategic interventions to support EAL students, emphasizing collaborative efforts between researchers and educators to bridge the gap between research and practice in diverse educational settings.
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- 2025
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79. A Scoping Review on U.S. Undergraduate Students with Disabilities in STEM Courses and STEM Majors
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Maura Borrego, Ariel Chasen, Hannah Chapman Tripp, Emily Landgren, and Elisa Koolman
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Background: The purpose of this scoping review is to describe how the literature has discussed and studied disability in undergraduate-level STEM courses in the United States. A Critical Disability Studies lens informed our inclusion criteria. Results: We considered extensive lists of disability types and diagnoses and concluded that "disability" as a search term best captured educational experiences rather than medical approaches. After screening nearly 9000 abstracts, we identified a final set of 409 dissertations, articles, conference papers, commentaries, briefs and news items. Sources appeared in discipline-based education research (DBER), STEM disciplinary and education journals as well as DBER conferences. Under 10% of sources included 2-year college settings. The largest groups of sources focused on disability writ large (39%, vs. specific categories) and across STEM (38%, vs. specific disciplines). Students were the main research participants (80%). Instructors were the main target of recommendations (84%). In terms of solutions, the largest group (n = 111) advocated for Universal Design, followed by accommodations (n = 94), and technology developed or tested with persons with disabilities (n = 90). Sources which the authors framed as empirical studies less frequently disclosed positionality as a person with a disability (16%) than non-empirical sources (21%). Quantitative (n = 125), qualitative (n = 99), and mixed methods (n = 64) approaches were well-represented. The most common data collection methods were surveys, assessments or task completions (n = 161 sources), followed by interviews (n = 109), observations (n = 44), document analyses (n = 18), and institutional student records (n = 14). Conclusions: More research is needed that centers the experiences of students with disabilities, focuses on specific disability types, employs critical quantitative and qualitative methodologies, and otherwise avoids implicit deficit views of disabled students. Citations to the qualifying sources are available in a public Zotero library.
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- 2025
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80. Psychometric Synthesis of the Drug Abuse Screening Test (DAST) Versions
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Erin Johnson, Samantha Barstack, Yikai Xu, Hannah Wise, Bradley T. Erford, Catharina Chang, and David Delmonico
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Problem Statement: Among individuals aged 12 years or older, 14.3% (40.0 million) reporting the use of an illicit drug in the previous year. Given the prevalence of drug abuse, it is increasingly important to determine effective screening practices, treatment procedures, and best practices among various subpopulations to identify drug use-related consequences. The DAST is one of the most commonly used and accurate drug screening tests. Method: This psychometric synthesis of four versions of the Drug Abuse Screening Test (DAST-10, DAST-20, DAST-28, DAST-A) provided aggregated evidence from 346 articles over 40 years of published literature for score reliability, structure, diagnostic, and convergent validity, and descriptive statistics, all with the goal of informing counseling and medical practice and research. Results: Results indicated adequate internal consistency ([alpha] = 0.81-0.84 across all four versions) and mostly medium to large effect size convergent correlations with comparison measures. Aggregated diagnostic validity data indicate optimal cutoff scores of 7 for DAST-10, 8 for DAST-20, 10 for DAST-28, and 6 for DAST-A. Discussion: The DAST-10 appears the best choice for practical and psychometric reasons. Additional studies of the various DAST versions are needed to expand use across participant demographics. Public Statement of Relevance: Drug use continues to be a societal problem and mental health practitioners need effective screening practices, treatment procedures, and best practices among various subpopulations to identify drug use-related consequences. This study synthesized 40 years of research on the four versions of the Drug Abuse Screening Test (DAST-10, DAST-20, DAST-28, DAST-A) and found acceptable levels of score reliability and validity for screening purposes.
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- 2025
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81. The Technical Adequacy of Coding Procedures for Retell Measures in Elementary School Students with Dyslexia
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Lauren E. Fennimore, Emma C. Pursley, Rachel E. Joyner, Hannah R. Manning, Nikita M. Pike, and Elizabeth B. Meisinger
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This study examined the psychometric properties of two common frameworks for scoring retell data (i.e., clause- and idea unit-based methods) among 86 third- through fifth-grade students with dyslexia. At the beginning and end of the school year, students read two grade-level R-CBM probes (one orally and one silently) and engaged in a retell following each passage. Results from the clause- and idea unit-based scoring frameworks were compared to a criterion measure, the GORT-5 Reading Comprehension subtest. Both retell scoring methods demonstrated adequate temporal stability across the school year and were also sensitive to detecting growth in student's comprehension skills across the year. Clause- and idea unit-based scores were found to correlate strongly with one another and correlated moderately to strongly with the criterion measure. Although differences did not emerge in the correlational results, mean differences did emerge with clause-based scoring resulting in a higher percentage of recall than did idea-units. Overall, results from this study provide support for the validity of retell as a measure of reading comprehension among elementary school students with dyslexia.
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- 2025
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82. A Comparative Study of How Teachers Communicate in Deaf Education Classrooms
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Leala Holcomb, Hannah Dostal, and Kimberly Wolbers
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This study investigates the communication practices of four teachers in 3rd to 6th grade classrooms with 9 deaf students with limited language proficiency and in stages of emergent writing development. Analyzing language modalities, utterance types, and class interactivity, we found that teachers using American sign language used student-centered approaches, generating a greater number of directives and responsive utterances. They persevered in increasing students' engagement and were successful in clarifying misunderstandings. Teachers using spoken English used teacher-centered approaches, making general comments directed at the whole class, which consequently reduced student participation and responsiveness. They also largely avoided repairing communication breakdowns with emergent writers, focusing instead on those with greater auditory and speaking abilities. These patterns reveal disparities in classroom communication that can affect student learning. Our findings highlight the need for teacher preparation programs to equip teachers with skill sets to employ accessible and effective communication during instruction, especially with deaf students who are still developing foundational language and writing skills.
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- 2025
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83. School Engagement, Epilepsy Severity, and Frequency of Victimization in School Age Children
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Jessica Emick, Nathan M. Griffith, and Hannah Schweitzer
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Epilepsy is one of the most common neurological disorders in young people, which disrupts daily life and results in an increased risk of victimization. Archival data from the 2018/2019 National Survey of Children's Health (NSCH), a nationally representative cross-sectional survey, were used. Data from the NSCH were collected via parent reports and analyzed for children aged 6-17 years (N = 25500). The results indicated that children with epilepsy (CWE) were significantly more likely to be frequently bullied than children with less stigmatizing chronic health conditions (i.e., asthma) and typical peers, but there was no significant association between the severity of epilepsy and the frequency of victimization. Furthermore, the degree of school engagement did not significantly moderate the relationship between epilepsy severity and frequency of victimization. However, the degree of school engagement was associated with the frequency of victimization in CWE, such that more school engagement was associated with less parent-reported victimization. Overall, these findings support the growing evidence that CWE are at a higher risk of being bullied and need unique interventions regardless of epilepsy severity and that school engagement should be further examined to reduce victimization among CWE.
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- 2025
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84. The Measurement of College Athletes' Knowledge and Behavior on Pre- and Post-Workout Nutrition Utilizing a Text Message Intervention
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Hannah Young, Julie R. Schumacher, Scott Pierce, and Jennifer L. Barnes
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Objective: The purpose of this study was to measure college student athletes' nutrition knowledge and behavior before and after a text message-based educational intervention. Participants: Athletes (n = 35) participated by completing a pre- and post-intervention survey. Methods: This survey gathered information on ability to identify carbohydrate and protein food sources, pre- and post-workout intake, and behavior. Text messages were sent during the 4-week intervention and included information regarding the importance of carbohydrate consumption before training, and a carbohydrate-protein mixture for recovery. Results: Dependent t-tests revealed a lack of statistically significant increases in total knowledge (p = 0.156) or behavior (p = 0.177), but an increase in the behavior questions regarding efficacy of carbohydrate before training (p = 0.026) and carbohydrate and protein after training (p = 0.016). Conclusion: This suggests the text message educational intervention did influence behavioral outcomes. Future research should focus on the effectiveness, length, and frequency of the text message intervention, and investigate the athletes' willingness to change dietary behaviors.
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- 2025
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85. Effects of Emergent Bilingual Students' and Their Teacher's Talk-Turns on Students' Comprehension during Read-Aloud Discussions
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Tanya Christ, Hyonsuk Cho, Ming Ming Chiu, Iman Bakhoda, Hannah Klebba, and Haylie Brown
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This article explores the unique contributions of 2nd-grade emergent bilingual (EB) students' talk-turns vs. those of their teacher on students' expressions of comprehension during interactive read-aloud discussions in a pull-out classroom. They engaged in eight video-recorded, interactive read-aloud discussions across four books. This mixed methods study used emergent coding and constant comparative analysis to identify codes that reflected each talk-turn in the discussion. Then, statistical discourse analysis determined how previous talk-turns (3,419 in all) impacted subsequent talk-turns in which students expressed comprehension (i.e. providing information, a connection/comparison/contrast, inference, or opinion). Findings show that EB students more often engaged in several talk-turns that predicted students' subsequent expressions of comprehension as compared to their teacher. Likewise, the teacher also engaged more often in certain talk-turns that predicted students' subsequent expressions of comprehension as compared to students. Further, teachers sometimes engaged in talk-turns that "reduced" students' likelihood of expressing comprehension. Thus, teachers should explicitly create space for student-initiated participation during interactive read-alouds.
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- 2025
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86. Gratitude for Bystander Action Varies by Peer Intervention and Social Norms
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Hannah Nguyen, Karin S. Frey, Huiyu Lin, and Adaurennaya C. Onyewuenyi
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Gratitude is associated with increased social integration, which may counter the loneliness stemming from repeated peer victimization. The gratitude youth feel after different types of bystander action may depend on which behaviors are most congruent with personal beliefs. Face and honor cultures provide social norms for expectations and interpretations of behavior, including how to act during and after interpersonal conflict. In the current study, 264 ethnically diverse adolescents (African, European, Mexican-American, and Indigenous) from the Pacific Northwest described past instances when they experienced an act of peer-instigated aggression and subsequent bystander action. We examined how face and honor endorsements predicted victims' gratitude following three different types of bystander action (calm, avenge, and reconcile). Face endorsement predicted higher gratitude for being calmed, whereas honor endorsement predicted higher gratitude for being avenged. Bystander efforts to reconcile conflict elicited the most gratitude but were not related to social norm endorsement. Our findings shed light on the association between social norm endorsement and victimized youths' gratitude for their intervening peers' bystander actions.
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- 2025
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87. Exploring Student Consensus about Module-Level Ethnicity Awarding Gaps: A Delphi Approach
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Kei Long Cheung, Michael Thomas, Billy Wong, Laura Hills, Hannah Froome, Nicholas Worsfold, and Daniel P. Bailey
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Attention paid to awarding gaps in higher education linked to ethnicity tends to focus on outcomes at the final award stage. Our project sought to scrutinise awarding gaps at module level where these gaps may emerge. Our aim was twofold: to identify the most important barriers to student success and determine strategies to reduce awarding gaps at module level, as perceived by students from various ethnic backgrounds and to investigate to what extent there is consensus amongst students of various ethnic backgrounds regarding these barriers and strategies. We employed a two-phase Delphi approach. The first phase involved data analytics to identify modules with awarding gaps in health and life sciences undergraduate degree programmes. The second phase employed a Delphi approach to collect student feedback on barriers to success and strategies to overcome them, focusing on culture, curriculum, and assessment. The study engaged 36 students in the first round and 53 in the second round. Our research confirmed the existence of awarding gaps at the module level. Students reached consensus on 55 out of 79 factors affecting their academic performance, with notable differences between White and racially minoritised student groups. This study suggests that, to close awarding gaps, both a module-level approach and a deep commitment to listening to our students is needed. Our study is the first to use a consensus-driven Delphi approach to identify key barriers and strategies at the module level, offering a framework for addressing awarding gaps and fostering inclusive, equitable education within and beyond the UK.
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- 2025
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88. Estimating Climate Change Numbers: Mental Computation Strategies That Can Support Science Learning
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Ian Thacker, Hannah French, and Shon Feder
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Presenting novel numbers about climate change to people after they estimate those numbers can shift their attitudes and scientific conceptions. Prior research suggests that such science learning can be supported by encouraging learners to make use of given benchmark information, however there are several other numerical estimation skills that may also be relevant in this context. This design-based research project aimed to identify specific mathematical skills that might support postsecondary students' learning from novel scientific data. Concurrently, we also developed an open-source online science learning app. In three design iterations, we conducted 22 think-aloud interviews with undergraduate and graduate students at a Hispanic Serving Institution as they estimated climate change data, before being shown the scientifically accepted value. Productive estimation strategies included: tolerance for error, mental computation skills (rounding and arithmetically manipulating given benchmark values), and integration of prior educational and personal experiences. Two cases are presented, the first illustrates a student who used estimation strategies productively and was tolerant of their inaccuracies, and the second illustrates a student who reacted negatively to feedback on their inaccuracies. Results implicate principles for integrating mathematics and science learning and showcase a learning intervention that embodies those principles.
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- 2025
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89. What Is a Biology Degree without a Career Goal or a Strategy to Reach That Goal? An Analysis of Career Goals of Graduating Biology Majors
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Melissa McCartney, Roxana Gonzalez, Jessica Colon, Laura Moralejo, Tessy Ritchie, Hannah Bruce Opris, Robin Castellano, Dania Hernandez, and Paola Freyre
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Improving the rate at which individuals enter STEM careers remains a national concern, and there is a critical need for additional research examining the impact of career development programs for undergraduate STEM majors. Most career development studies are grounded in Social Cognitive Career Theory (SCCT), which states that students progressing toward careers need to identify a career goal and implement strategies to reach this goal. We surveyed Biology majors in their final semester to learn more about the participant's career goal(s) and the strategies they implement to reach these goals. We found that most students have only one career goal and that this goal is most likely centered in the health sciences. Additionally, we show that students are lacking in specific strategies to reach these goals and that strategies students do provide do not always integrate with their career goals. These data suggest that graduating Biology majors are not aligned with the central tenants of SCCT.
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- 2025
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90. A LatCrit Analysis of Latina Collegians' Recollections of Racism in Educational Spaces
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Hannah L. Reyes and Antonio Duran
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Often noted in educational scholarship is Latinx/a/o students' experiences of racism that they encounter from peers, faculty, and staff within college contexts. Less discussed, however, are the persistent long-term effects of everyday, racist messages on Latinx/a/o students they received in PreK-12 years and how these forms of marginalization in turn affect how they navigate through college. Using a narrative methodological approach and Latino critical race theory as a framework, we centered the experiences of 12 Latina college students to investigate this question. Findings revealed how these Latinas' experiences of racism in PreK-12 were shaped by interactions involving family, relying upon their mothers' teachings in the process. However, when they transitioned to college, their understandings of racism as previously informed by their mothers evolved and became more complex. We then provide implications for practice.
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- 2025
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91. 'Virtual Biostats Day': An Interactive Online Biostatistics Outreach Program Directed at High School Students in Groups Historically Underrepresented in STEM Careers
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Esther Drill, Jessica A. Lavery, Stephanie Lobaugh, Jessica Flynn, Samantha Brown, Hannah Kalvin, Joanne F. Chou, David Nemirovsky, Zoe Guan, Sujata Patil, and Kay See Tan
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Persistent underrepresentation of Black and Hispanic Statistics degree holders relative to the U.S. population occurs at all levels in post-secondary education, contributing to the underrepresentation of Black and Hispanic Bio/Statisticians. Attempting to address this inequity before the undergraduate level, Memorial Sloan Kettering (MSK)'s Bridge to Biostats committee was established with the mission to increase awareness of and interest in the field of Biostatistics among high school students in groups underrepresented in STEM fields. We describe in this article an outreach program targeted to underrepresented students in New York City with innate aptitude and interest in STEM subjects, but minimal exposure to Statistics. Virtual Biostats Day is a 1-1/4 hr online exposure program that incorporates interactive elements exploring statistical concepts alongside clear descriptions of the field of Biostatistics and the roles and responsibilities of a Biostatistician. Students are also introduced to professional Biostatisticians who describe their journeys from high school to where they are today. This combination of didactic and active learning has engaged students, even in the online environment. We relate our experience in implementing and presenting our program to students and provide a complete toolkit for other Bio/Statistics departments or organizations interested in engaging in similar outreach work. Supplementary materials for this article are available online.
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- 2025
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92. Reaching the Finish Line: Can Coaching Help Students Graduate?
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Kelly Lack and Hannah Acheson-Field
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Many students who enter college do not finish, reflecting numerous academic, financial, and social barriers to postsecondary completion. Success Boston Coaching (SBC) provides students in the Greater Boston area with coaching during their first 2 years of college to help them overcome barriers to postsecondary completion and connect them to supports at their campuses and in their communities. We estimate the differences between SBC and comparison students with respect to students' postsecondary completion rates 4, 5, and 6 years after they initially enroll in postsecondary education. Using administrative outcome data from the National Student Clearinghouse and 11 institutions of higher education in the Greater Boston area, we follow coached students and a matched group of similar noncoached students up to 6 years after their initial enrollment. Relative to noncoached students, SBC students had associate's degree completion rates that were 85 percent higher after 4 years, 44 percent higher after 5 years, and 63 percent higher after 6 years. SBC students also completed bachelor's degrees within 5 years at a 12 percent higher rate than their noncoached peers. Results suggest that coaching that offers comprehensive, personalized, and regularly occurring support to students is associated with higher completion rates, especially for students pursuing associate's degrees. Additional efforts to directly remove barriers that students face may help more students graduate.
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- 2025
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93. Integrating Diversity into Psychology Statistics Courses: Advice, Reflections, and Special Considerations
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Ruth V. Walker, Hannah Osborn, Julie Madden, and Kristen Jennings Black
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Introduction: In an increasingly diverse world, there has been a call for psychology educators to make efforts to integrate diversity into the psychology curriculum. Statement of the Problem: Researchers who have surveyed psychology faculty have found the amount of time devoted to diversity content in nondiversity-focused courses is limited, with faculty citing barriers to integration such as a lack of relevance and time constraints. Literature Review: Educators who have worked to incorporate diversity topics into their courses have found positive outcomes. Teaching Implications: We describe our approach to integrating diversity into an introductory statistics course, highlight lessons learned, and provide special considerations for psychology educators. Conclusion: Integrating diversity-related content into statistics is challenging; however, the effort is worth it, particularly if integrating diversity content into required courses may be the only exposure to diversity students get.
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- 2025
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94. Teaching Constructive Replications in the Behavioral and Social Sciences Using Quantitative Data
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Gerrit Bauer, Nate Breznau, Johanna Gereke, Jan H. Höffler, Nicole Janz, Rima-Maria Rahal, Joachim K. Rennstich, and Hannah Soiné
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Introduction: The replication crisis in the behavioral and social sciences spawned a credibility revolution, calling for new open science research practices that ensure greater transparency, including preregistrations, open data and code, and open access. Statement of the Problem: Replications of published research are an important element in this revolution as part of the self-correcting process of scientific knowledge production; however, the teaching value of replications is still underutilized thus far. Literature Review: Pedagogical knowledge points to the value of replication as critical to the scientific method of test and retest. Psychology has already begun mass efforts to reproduce previous experiments. Yet, we have very few examples of how analytical and reanalysis replications, after the data come in, contribute to the reproducibility crisis and can be integrated into undergraduate and graduate courses. Teaching Implications: Replications with quantitative data can be a pedagogical tool for improving student research method skills and introducing them to best research practices via learning-by-doing. Conclusion: This article aims to start filling this gap by offering guidance to instructors in designing and teaching replications for students at various levels and disciplines in the social and behavioral sciences, including a supplementary teaching companion.
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- 2025
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95. Relations between Distinct Dimensions of Physical Activity and Preschoolers' ADHD Symptoms
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Erin K. Shoulberg, Hannah Scott, Caroline P. Martin, Connie L. Tompkins, Marissa Dennis, Allison Krasner, and Betsy Hoza
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Objective: This study examined the unique and interactive effects of PA volume and within-person fluctuations in PA volume (i.e., intraindividual variability in PA volume; PA-var) on preschoolers' (N = 141; 47.5% girls) ADHD symptoms. Method: Preschoolers wore accelerometers during the school day over a 2-week period. Teachers reported on children's hyperactive, impulsive, and inattentive symptoms on the ADHD Rating Scale--IV Preschool Version. Results: In the context of regression models, higher levels of PA-var were linked with lower levels of impulsive symptoms. Higher levels of PA volume were linked with higher levels of hyperactive and inattentive symptoms, but only when PA fluctuation (i.e., PAvar) was relatively low. Post hoc analyses with teacher-reported impairment as the outcome revealed a significant interaction such that the positive association between PA volume and impairment was stronger at lower, as compared to higher, levels of PA-var. Conclusion: Larger fluctuations in preschoolers' PA volume may indicate lower risk for displaying impulsive symptoms. Moreover, preschoolers with high levels of PA that remain relatively consistent throughout the day may be at increased risk for exhibiting hyperactive and inattentive symptoms and related impairment, suggesting they are unable to regulate their activity to meet expectations in the school environment.
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- 2025
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96. Role of imaging in the diagnosis and management of mixed epithelial and stromal tumors of the kidney: Case series and comprehensive review.
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Chen, Kasha, Zhao, Jasmine, Sasani, Ali, Sabour, Ryan, Cho, Hannah, Bansal, Riya, Shu, Chang, OConnell, Ryan, and Houshyar, Roozbeh
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Kidney ,Kidney tumors ,Mixed epithelial and stromal tumor - Abstract
Mixed epithelial and stromal tumor (MEST) of the kidney is a rare benign neoplasm composed of both stromal and epithelial components. MEST is mainly seen in adults with a strong predilection for perimenopausal women with history of hormone replacement therapy. While MEST is generally benign, there are reported cases of malignant transformation and adverse clinical outcomes. This case series will present 7 cases of this rare renal neoplasm with emphasis on radiological imaging as an important tool in the guidance of clinical management. Considering the rarity of this tumor and its variable presentation, understanding the radiological features on multiple modalities can guide appropriate clinical and surgical management of MEST patients.
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- 2025
97. Same data, different analysts: variation in effect sizes due to analytical decisions in ecology and evolutionary biology.
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Gould, Elliot, Fraser, Hannah, Parker, Timothy, Nakagawa, Shinichi, Griffith, Simon, Vesk, Peter, Fidler, Fiona, Hamilton, Daniel, Abbey-Lee, Robin, Abbott, Jessica, Aguirre, Luis, Alcaraz, Carles, Aloni, Irith, Altschul, Drew, Arekar, Kunal, Atkins, Jeff, Atkinson, Joe, Baker, Christopher, Barrett, Meghan, Bell, Kristian, Bello, Suleiman, Beltrán, Iván, Berauer, Bernd, Bertram, Michael, Billman, Peter, Blake, Charlie, Blake, Shannon, Bliard, Louis, Bonisoli-Alquati, Andrea, Bonnet, Timothée, Bordes, Camille, Bose, Aneesh, Botterill-James, Thomas, Boyd, Melissa, Boyle, Sarah, Bradfer-Lawrence, Tom, Bradham, Jennifer, Brand, Jack, Brengdahl, Martin, Bulla, Martin, Bussière, Luc, Camerlenghi, Ettore, Campbell, Sara, Campos, Leonardo, Caravaggi, Anthony, Cardoso, Pedro, Carroll, Charles, Catanach, Therese, Chen, Xuan, Chik, Heung, Choy, Emily, Christie, Alec, Chuang, Angela, Chunco, Amanda, Clark, Bethany, Contina, Andrea, Covernton, Garth, Cox, Murray, Cressman, Kimberly, Crotti, Marco, Crouch, Connor, DAmelio, Pietro, de Sousa, Alexandra, Döbert, Timm, Dobler, Ralph, Dobson, Adam, Doherty, Tim, Drobniak, Szymon, Duffy, Alexandra, Duncan, Alison, Dunn, Robert, Dunning, Jamie, Dutta, Trishna, Eberhart-Hertel, Luke, Elmore, Jared, Elsherif, Mahmoud, English, Holly, Ensminger, David, Ernst, Ulrich, Ferguson, Stephen, Fernandez-Juricic, Esteban, Ferreira-Arruda, Thalita, Fieberg, John, Finch, Elizabeth, Fiorenza, Evan, Fisher, David, Fontaine, Amélie, Forstmeier, Wolfgang, Fourcade, Yoan, Frank, Graham, Freund, Cathryn, Fuentes-Lillo, Eduardo, Gandy, Sara, Gannon, Dustin, García-Cervigón, Ana, Garretson, Alexis, Ge, Xuezhen, Geary, William, Géron, Charly, and Gilles, Marc
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Analytical heterogeneity ,Many-analyst ,Metascience ,Replication crisis ,Reproducibility ,Ecology ,Biological Evolution ,Animals ,Passeriformes ,Eucalyptus - Abstract
Although variation in effect sizes and predicted values among studies of similar phenomena is inevitable, such variation far exceeds what might be produced by sampling error alone. One possible explanation for variation among results is differences among researchers in the decisions they make regarding statistical analyses. A growing array of studies has explored this analytical variability in different fields and has found substantial variability among results despite analysts having the same data and research question. Many of these studies have been in the social sciences, but one small many analyst study found similar variability in ecology. We expanded the scope of this prior work by implementing a large-scale empirical exploration of the variation in effect sizes and model predictions generated by the analytical decisions of different researchers in ecology and evolutionary biology. We used two unpublished datasets, one from evolutionary ecology (blue tit, Cyanistes caeruleus, to compare sibling number and nestling growth) and one from conservation ecology (Eucalyptus, to compare grass cover and tree seedling recruitment). The project leaders recruited 174 analyst teams, comprising 246 analysts, to investigate the answers to prespecified research questions. Analyses conducted by these teams yielded 141 usable effects (compatible with our meta-analyses and with all necessary information provided) for the blue tit dataset, and 85 usable effects for the Eucalyptus dataset. We found substantial heterogeneity among results for both datasets, although the patterns of variation differed between them. For the blue tit analyses, the average effect was convincingly negative, with less growth for nestlings living with more siblings, but there was near continuous variation in effect size from large negative effects to effects near zero, and even effects crossing the traditional threshold of statistical significance in the opposite direction. In contrast, the average relationship between grass cover and Eucalyptus seedling number was only slightly negative and not convincingly different from zero, and most effects ranged from weakly negative to weakly positive, with about a third of effects crossing the traditional threshold of significance in one direction or the other. However, there were also several striking outliers in the Eucalyptus dataset, with effects far from zero. For both datasets, we found substantial variation in the variable selection and random effects structures among analyses, as well as in the ratings of the analytical methods by peer reviewers, but we found no strong relationship between any of these and deviation from the meta-analytic mean. In other words, analyses with results that were far from the mean were no more or less likely to have dissimilar variable sets, use random effects in their models, or receive poor peer reviews than those analyses that found results that were close to the mean. The existence of substantial variability among analysis outcomes raises important questions about how ecologists and evolutionary biologists should interpret published results, and how they should conduct analyses in the future.
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- 2025
98. Pre-conception clinical risk factors differ between spontaneous and indicated preterm birth in a densely phenotyped EHR cohort
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Costello, Jean M, Takasuka, Hannah, Roger, Jacquelyn, Yin, Ophelia, Tang, Alice, Oskotsky, Tomiko, Sirota, Marina, and Capra, John A
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Reproductive Medicine ,Midwifery ,Biomedical and Clinical Sciences ,Health Sciences ,Clinical Research ,Prevention ,Women's Health ,Pregnancy ,Maternal Health ,Cardiovascular ,Health Disparities ,Preterm ,Low Birth Weight and Health of the Newborn ,Pediatric ,Patient Safety ,Perinatal Period - Conditions Originating in Perinatal Period ,2.1 Biological and endogenous factors ,4.1 Discovery and preclinical testing of markers and technologies ,Reproductive health and childbirth ,Good Health and Well Being ,Humans ,Female ,Premature Birth ,Electronic Health Records ,Risk Factors ,Adult ,Cohort Studies ,Infant ,Newborn ,Young Adult ,Maternal Age ,Diagnosis associations ,Electronic health records ,Indicated preterm birth ,Spontaneous preterm birth ,Nursing ,Paediatrics and Reproductive Medicine ,Public Health and Health Services ,Obstetrics & Reproductive Medicine ,Reproductive medicine - Abstract
BackgroundPreterm birth (PTB) is the leading cause of infant mortality. Risk for PTB is influenced by multiple biological pathways, many of which are poorly understood. Some PTBs result from medically indicated labor following complications from hypertension and/or diabetes, while many others are spontaneous with unknown causes. Previously, investigation of potential risk factors has been limited by a lack of data on maternal medical history and the difficulty of classifying PTBs as indicated or spontaneous. Here, we leverage electronic health record (EHR) data (patient health information including demographics, diagnoses, and medications) and a supplemental curated pregnancy database to overcome these limitations. Novel associations may provide new insight into the pathophysiology of PTB as well as help identify individuals who would be at risk of PTB.MethodsWe quantified associations between maternal diagnoses and preterm birth both with and without controlling for maternal age and socioeconomic factors within a University of California, San Francisco (UCSF), EHR cohort with 10,643 births (nterm = 9692, nspontaneous_preterm = 449, nindicated_preterm = 418) and maternal pre-conception diagnoses derived from International Classification of Diseases (ICD) 9 and 10 codes.ResultsThirty diagnoses significantly and robustly (False Discovery Rate (FDR)
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- 2025
99. Hybrid Telemedicine and In-Person Care for Kidney Transplant Follow-Up: A Qualitative Study.
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Esayed, Suad, Kim, Ellie, Sung, Hannah, Al-Seraji, Abdula, Adeyemo, Simeon, Troutt, Hayden, Tantisattamoa, Ekamol, Ferrey, Antoney, Reddy, Uttam, Malik, Fatima, Redfield, Robert, Ichii, Hirohito, Muzaale, Abimereki, Malhotra, Divyanshu, and Al Ammary, Fawaz
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access to healthcare ,attitudes ,continuity of care ,kidney transplantation ,qualitative research ,telehealth ,Humans ,Telemedicine ,Female ,Kidney Transplantation ,Male ,Middle Aged ,Qualitative Research ,Follow-Up Studies ,Aged ,Prognosis ,Transplant Recipients ,Kidney Failure ,Chronic - Abstract
BACKGROUND: Kidney transplant recipients are immunocompromised and require lifelong follow-up. Recipients face geographic, socioeconomic, and logistical challenges when seeking follow-up that can be alleviated using telemedicine. We aimed to understand patient experiences and preferences regarding telemedicine video visits and highlight insights to advance adopting hybrid telemedicine/in-person transplant care. METHODS: We conducted qualitative in-depth, semi-structured interviews with kidney transplant recipients between November 18, 2022, and January 11, 2023. Participants had follow-up at ≥12 months post-transplant via telemedicine at a tertiary transplant center. Study enrollment continued until data saturation was reached (n = 20 participants) when no new information emerged from additional interviews. Transcripts were analyzed using inductive thematic analysis. RESULTS: Participants median age was 58 years (IQR, 52-72), and 50% were female, 45% were White, 30% were Black, 15% were Asian, 10% were Hispanic/Other persons, and 30% were out-of-state residents. We identified the following seven themes: (1) reducing travel time, (2) minimizing financial burden (decreasing travel-related expenses and lost wages), (3) engaging patients within their comfort space, (4) establishing rapport with patients, (5) limitations of the virtual physical exam, (6) enhancing access to transplant providers (maximizing adherence to follow-up), and (7) lowering risk of communicable diseases. CONCLUSIONS: Integrating telemedicine with in-person visits enhances post-transplant follow-up care. A hybrid model should leverage the strengths of both modalities, ensuring patient access to care and being patient-centered and flexible. Efforts are needed to advance technological tools in physical examination and human connection, and assess patient outcomes. Policymakers and healthcare systems need to incentivize the adoption and expansion of telemedicine in transplant care.
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- 2025
100. Cover crop composition influences soil health and nematodes in a Mediterranean almond orchard system
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Wauters, Vivian M, Gaudin, Amélie CM, Haring, Steven, Hanson, Bradley D, Geoghan, Peter, Kim, Hannah, and Hodson, Amanda
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Agricultural ,Veterinary and Food Sciences ,Biological Sciences ,Ecology ,Environmental Sciences ,Forestry Sciences ,Generic health relevance ,Agricultural and Veterinary Sciences ,Agronomy & Agriculture ,Agricultural ,veterinary and food sciences ,Biological sciences ,Environmental sciences - Abstract
Planted cover crops and naturally occurring resident vegetation in orchards and agricultural fields belong to multiple functional groups which differ in traits, growth dynamics, and ability to influence soil health and biology. These different vegetation mixes may influence soil food webs and ecosystems over a growing season as well as regulating nutrient cycling outcomes. This study characterized soil organisms and nutrients as well as physical and chemical indicators of soil health in an almond orchard with three vegetation mixes: winter resident vegetation (dominated by Vicia, Malva, and Hordeum weedy species), and two cover crops mixes, one functionally diverse (brassica, legume, cereals) and one functionally uniform (brassicas), all of which had been implemented for four years. While physical soil health indicators differed seasonally, only biological indicators differed among the vegetation. During the winter and early spring growth period, both cover crop mixes increased microbial biomass nitrogen compared to resident vegetation, while the uniform mix supported greater numbers of nematodes overall, regardless of trophic group. The diverse cover crop mix supported more complex nematode food webs, as measured by the Structure Footprint, while nematode communities overall were primarily influenced by cover crop biomass and microbial biomass nitrogen. Soil nutrients increased after vegetation termination by flail mowing in all treatments, and the resident vegetation had the largest increase in dissolved organic carbon between the Cover and Post-Cover time points. Nematode indicators of food web composition and function decreased after termination, including the Structure Index, Fungal Footprint and Bacterivore Footprint. In contrast, the relative abundance of herbivores increased over time and, at the Post-Cover time point, both the diverse mix and the uniform mix had a higher proportion of root herbivores than resident vegetation, although these were primarily in the genus Tylenchorhynchus and not considered economically damaging. Increased processing of resources through generalist root herbivore nematodes may have been due to nematodes feeding on the cover crops themselves or on almond roots that foraged into soils with more nutrients. These results suggest that cover cropping can influence soil food webs on multiple fronts including root herbivores and predators. Greater knowledge of how cover crops influence soil health and for how long, during a season, will allow for the more efficient use of cover crops in Mediterranean systems.
- Published
- 2025
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