{"id":"W2959837313","doi":"10.2134/jeq2019.05.0220","title":"Agricultural Water Quality in Cold Climates: Processes, Drivers, Management Options, and Research Needs","year":2019,"lang":"en","type":"review","venue":"Journal of Environmental Quality","topic":"Soil and Water Nutrient Dynamics","field":"Environmental Science","cited_by":57,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada; Environment and Climate Change Canada; University of Waterloo; Global Institute for Water Security; University of Saskatchewan","funders":"Global Water Futures; Agricultural Research Service; U.S. Department of Agriculture; College of Engineering, Michigan State University; Canada First Research Excellence Fund; Michigan State University","keywords":"Agriculture; Water quality; Environmental science; Water resource management; Quality (philosophy); Cold climate; Climate change; Environmental resource management; Business; Natural resource economics; Environmental planning; Geography; Economics; Ecology; Meteorology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.004269596,0.0004275263,0.001316515,0.0002949903,0.0001514998,0.00009865532,0.0006263391,0.0002912745,0.0002083155],"category_scores_gemma":[0.00002703761,0.000261772,0.0002981226,0.0004363956,0.0004308291,0.0005088421,0.0009836105,0.0009641201,0.0003824551],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001563964,"about_ca_system_score_gemma":0.00002402602,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004805492,"about_ca_topic_score_gemma":0.000009033085,"domain_scores_codex":[0.9947903,0.0011447,0.001665542,0.0004637817,0.001319262,0.0006164304],"domain_scores_gemma":[0.9985162,0.0002423805,0.0006448481,0.0003694131,0.00001101417,0.0002161676],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004074627,0.007203672,0.4609876,0.08497433,0.001311847,0.0004902774,0.00690902,0.0002812966,0.0004975529,0.001580438,0.003820684,0.4315358],"study_design_scores_gemma":[0.001244503,0.0002419743,0.02830324,0.002904126,0.000317754,0.0001095307,0.002396306,0.000003054806,0.00001608628,0.0005614394,0.96314,0.0007619748],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.2340407,0.7626029,0.000003273708,0.0001097805,0.0003017407,0.001302142,0.0001662585,0.00001322261,0.001460082],"genre_scores_gemma":[0.01308364,0.9856215,0.0002124731,0.00002864225,0.00006549426,0.00002661338,0.00007619144,0.00002979815,0.0008556155],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9593194,"threshold_uncertainty_score":0.9999834,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0875491018182819,"score_gpt":0.373215228755707,"score_spread":0.285666126937425,"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."}}