Cite
KGML-ag: a modeling framework of knowledge-guided machine learning to simulate agroecosystems: a case study of estimating N2O emission using data from mesocosm experiments.
MLA
Liu, Licheng, et al. “KGML-Ag: A Modeling Framework of Knowledge-Guided Machine Learning to Simulate Agroecosystems: A Case Study of Estimating N2O Emission Using Data from Mesocosm Experiments.” Geoscientific Model Development, vol. 15, no. 7, July 2022, pp. 2839–58. EBSCOhost, https://doi.org/10.5194/gmd-15-2839-2022.
APA
Liu, L., Xu, S., Tang, J., Guan, K., Griffis, T. J., Erickson, M. D., Frie, A. L., Jia, X., Kim, T., Miller, L. T., Peng, B., Wu, S., Yang, Y., Zhou, W., Kumar, V., & Jin, Z. (2022). KGML-ag: a modeling framework of knowledge-guided machine learning to simulate agroecosystems: a case study of estimating N2O emission using data from mesocosm experiments. Geoscientific Model Development, 15(7), 2839–2858. https://doi.org/10.5194/gmd-15-2839-2022
Chicago
Liu, Licheng, Shaoming Xu, Jinyun Tang, Kaiyu Guan, Timothy J. Griffis, Matthew D. Erickson, Alexander L. Frie, et al. 2022. “KGML-Ag: A Modeling Framework of Knowledge-Guided Machine Learning to Simulate Agroecosystems: A Case Study of Estimating N2O Emission Using Data from Mesocosm Experiments.” Geoscientific Model Development 15 (7): 2839–58. doi:10.5194/gmd-15-2839-2022.