{"id":"W3021615978","doi":"10.1002/ird.2466","title":"Assessing water and nitrate‐N losses from subsurface‐drained paddy lands by DRAINMOD‐N II","year":2020,"lang":"en","type":"article","venue":"Irrigation and Drainage","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Drainage; Hydrology (agriculture); Effluent; Environmental science; Nitrate; Mean squared error; Animal science; Environmental engineering; Mathematics; Chemistry; Geology; Ecology; Statistics; 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":[0.000298415,0.000464582,0.0003166509,0.000234851,0.0001582543,0.0003410426,0.0004627498,0.0002281812,0.0003870997],"category_scores_gemma":[0.0003581951,0.0001620891,0.0003117262,0.000284995,0.0001939225,0.0003491301,0.000264801,0.0001723302,0.00005957709],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009568785,"about_ca_system_score_gemma":0.0004750739,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01093681,"about_ca_topic_score_gemma":0.01742611,"domain_scores_codex":[0.999923,0.00002140673,0.00000365188,0.00002440574,0.00001881021,0.000008701355],"domain_scores_gemma":[0.999853,0.0000619784,0.0000218285,0.00001469695,0.00003803756,0.00001043952],"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":[0.0009841633,0.0003160204,0.29581,0.0002911736,0.000151359,0.0002712856,0.0001981224,0.5777196,0.08172438,0.0003762598,0.0005961302,0.04156154],"study_design_scores_gemma":[0.00007561473,0.0004691595,0.08909352,0.00001501674,0.00007783256,0.00005592378,0.0001542807,0.8705886,0.03770082,0.0004390088,0.001294634,0.00003551465],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9962655,0.00002720772,0.002932268,0.000009160365,0.000003043264,0.00001424292,0.0001814752,0.00007348782,0.0004935779],"genre_scores_gemma":[0.9970484,0.00002825743,0.002467071,0.000006227583,9.32822e-7,0.00001756652,0.0002137191,0.000006905525,0.0002108518],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01093681,"threshold_uncertainty_score":0.02174628,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01441668527071435,"score_gpt":0.2258642422059902,"score_spread":0.2114475569352758,"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."}}