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Camera traps provide a low-cost approach to collect data and monitor wildlife across large scales but hand-labeling images at a rate that outpaces accumulation is difficult. Deep learning, a subdiscipline of machine learning and computer science, can address the issue of automatically classifying camera-trap images with a high degree of accuracy. This technique, however, may be less accessible to ecologists or small-scale conservation projects, and has serious limitations. In this study, we trained a simple deep learning model using a dataset of 120,000 images to identify the presence of nilgai Boselaphus tragocamelus, a regionally specific nonnative game animal, in camera-trap images with an overall accuracy of 97%. We trained a second model to identify 20 groups of animals and one group of images without any animals present, labeled as “none,” with an accuracy of 89%. Lastly, we tested the multigroup model on images collected of similar species, but in the southwestern United States, resulting in significantly lower precision and recall for each group. This study highlights the potential of deep learning for automating camera-trap image processing workflows, provides a brief overview of image-based deep learning, and discusses the often-understated limitations and methodological considerations in the context of wildlife conservation and species monitoring.

The presence of a soil seedbank facilitates the persistence of annual weed species in arable fields. Soil weed seedbank is replenished by many sources, but the largest one is the seeds produced by uncontrolled late-season weed escapes. The estimation of weed seed production potential from late-season escapes may allow farmers to make appropriate management decisions to minimize seedbank replenishment. The objective of this research was to evaluate the feasibility of using unmanned aerial vehicle–based RGB and multispectral imagery for estimating seed rain potential in late-season weed escapes in crop fields. Three case studies were used to capture images of weed escapes before crop harvest: common waterhemp [Amaranthus tuberculatus (Moq.) Sauer] in soybean [Glycine max (L.) Merr.], Palmer amaranth [Amaranthus palmeri (S.) Watson] in cotton (Gossypium hirsutum L.), and johnsongrass [Sorghum halepense (L.) Pers.] in soybean. Randomly selected quadrats with different density gradients of weed escapes were sampled at the time of crop maturity. High-resolution RGB and multispectral images of the experimental area were collected using drones immediately before ground sample collection. Normalized difference vegetation index (NDVI), excess green index (ExG), and canopy volume estimates derived from canopy height models were used to obtain weed biological measurements (biomass and seed production). Among the indices investigated, NDVI and ExG had very strong correlations (0.71 to 0.97) with weed biomass. No specific remote sensing variable was ideal across the three cases examined here, suggesting that a generalized remote sensing approach may not offer robust estimations and case-specific applications are imperative. Nonetheless, drone imagery is a powerful tool for estimating seed production from uncontrolled weed escapes and assisting with management decision making.

This qualitative study uses the content analysis method of Newman, Webb, and Cochrane to analyze the contents of the interactions of 21 discussion forums; we succeeded in identifying different types of critical thinking. Carried out in a private institution in the north of Mexico, we sampled three groups with a total of 74 students. Discussion forums were organized using the taxonomy of Socratic questions in order to increase critical thinking. We conclude with guidelines to design quality discussion forums to increase and develop critical thinking in virtual education.

Resumen tomado de la publicación Este estudio cuantitativo mide la percepción del Aprendizaje Cognitivo, Afectivo y Psicomotor en un curso académico presencial con apoyo en plataforma educativa; a través del instrumento Escala de percepción del aprendizaje (Rovai, et al., 2009). Analizando los resultados obtenidos de la medición estadística de la T-Student, con grupo control y experimental con un diseño cuasi-experimental con pos-prueba únicamente, es posible dar respuesta al planteamiento del problema y las hipótesis planteadas en este estudio. Llegando a reflexionar y discutir sobre la posibilidad de incrementar la percepción del aprendizaje cognitivo, afectivo y psicomotor al implementar Foros de Discusión en un curso académico de Educación Superior. Universidad de Oviedo. Biblioteca de Formación del Profesorado y Educación; Calle Aniceto Sela s/n; 33005 Oviedo; Tel. +34985103215; Fax +34985103214; buoccee@uniovi.es ESP

espanolRESUMENEste estudio cuantitativo mide la percepcion del Aprendizaje Cognitivo, Afectivo y Psicomotor en un curso academico presencial con apoyo en plataforma educativa; a traves del instrumento Escala de percepcion del aprendizaje (Rovai, et al., 2009). Ana­lizando los resultados obtenidos de la medicion estadistica de la T-Student, con grupo control y experimental con un diseno cuasi-experimental con pos-prueba unicamente, es posible dar respuesta al planteamiento del problema y las hipotesis planteadas en este estudio. Llegando a reflexionar y discutir sobre la posibilidad de incrementar la percepcion del aprendizaje cognitivo, afectivo y psicomotor al implementar Foros de Discusion en un curso academico de Educacion Superior.PALABRAS CLAVE: Aprendizaje Cognitivo; Afectivo y Psicomotor; Foros de Discusion EnglishABSTRACT This quantitative study measures the perception of cognitive, affective and psycho­motor learning in an academic course with educational platform support; scale through the instrument of perception of learning (Rovai, et al., 2009). Analyzing the statistical results of the T-Student, with a control and experimental group within a quasi-experi­mental design with posttest only, it´s possible to respond to the statement problem and hypothesis made in this study. Coming to reflect and discuss the possibility of increa­sing the perception of cognitive, affective and psychomotor learning by implementing Discussion Forums in an academic course of Higher Education. KEYWORDS : Cognitive; Affective and Psychomotor Learning; Discussion Forums