{"id":"W4403197293","doi":"10.1002/jeq2.20633","title":"Watershed‐scale spatial prediction of agricultural land phosphorus mass balance and soil phosphorus metrics: A bottom‐up approach","year":2024,"lang":"en","type":"article","venue":"Journal of Environmental Quality","topic":"Soil and Water Nutrient Dynamics","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut de Recherche et de Développement en Agroenvironnement","funders":"Lake Champlain Basin Program; U.S. Geological Survey; Vermont Agency of Agriculture Food and Markets; National Oceanic and Atmospheric Administration; U.S. Environmental Protection Agency","keywords":"Watershed; Environmental science; Hydrology (agriculture); Water balance; Agricultural land; Agriculture; Soil texture; Soil water; Soil science; Geography; Geology; Computer science; Geotechnical engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003524289,0.0004126502,0.0004195817,0.001125336,0.0004177875,0.0009556422,0.0005192054,0.0003337872,0.0006626918],"category_scores_gemma":[0.0009435554,0.000305979,0.0005022939,0.0009612985,0.0002564212,0.0004744554,0.0005514191,0.0002990707,0.0001475033],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001106594,"about_ca_system_score_gemma":0.0009813407,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07202137,"about_ca_topic_score_gemma":0.0575843,"domain_scores_codex":[0.999885,0.00002719954,0.00000567721,0.00004089275,0.0000262473,0.00001495454],"domain_scores_gemma":[0.9997374,0.00009723378,0.00003281429,0.00002411935,0.00008500867,0.00002348325],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000630966,0.0001111852,0.07662657,0.00002716524,0.0001144517,0.00008386009,0.00006967724,0.8794525,0.005971604,0.0005131393,0.0004832009,0.03648349],"study_design_scores_gemma":[0.000002709067,0.00000624221,0.008485613,0.000001951368,0.000005864746,0.000002431613,0.00001622287,0.9908832,0.0003371588,0.0001719646,0.00008338614,0.000003292144],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8980621,0.00009598746,0.09702001,0.0001728551,0.00001177388,0.00008018973,0.0009901922,0.001034987,0.002531762],"genre_scores_gemma":[0.9762285,0.00003214771,0.02292863,0.00001121993,0.000004721855,0.0000296308,0.0003972068,0.00002515602,0.0003426854],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07202137,"threshold_uncertainty_score":0.1432043,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01008438825828139,"score_gpt":0.2118864314367535,"score_spread":0.2018020431784721,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}