{"id":"W3113856913","doi":"10.3390/hydrology8010001","title":"Assessment of Impacts of Climate Change on Tile Discharge and Nitrogen Yield Using the DRAINMOD Model","year":2020,"lang":"en","type":"article","venue":"Hydrology","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of the Environment, Conservation and Parks; University of Waterloo; University of Guelph","funders":"Ministry of Agriculture, Food and Rural Affairs; Ontario Ministry of Agriculture, Food and Rural Affairs","keywords":"Environmental science; Hydrology (agriculture); Evapotranspiration; Tile drainage; Water quality; Water table; Climate change; Precipitation; Soil and Water Assessment Tool; Point source pollution; Streamflow; Nonpoint source pollution; Groundwater; Soil water; Meteorology; Soil science; Drainage basin; Geography; Ecology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001627953,0.0000872356,0.0001756642,0.00001689746,0.00008748094,0.00000195176,0.0001134859,0.00004564012,0.0001088599],"category_scores_gemma":[0.00001368927,0.00005819305,0.0000306398,0.00005930441,0.0002657842,0.00005864486,0.0003404615,0.00008068951,0.000007415613],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001017992,"about_ca_system_score_gemma":0.000002025787,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008053381,"about_ca_topic_score_gemma":0.00002506358,"domain_scores_codex":[0.9993545,0.00004635098,0.0001366348,0.000170904,0.00008849851,0.0002030581],"domain_scores_gemma":[0.9997052,0.00004473064,0.00008947698,0.0001209265,0.000002189603,0.00003749624],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001258464,0.0001456694,0.9218332,0.00007714472,0.0001215048,0.000007165188,0.005031684,0.04008939,0.02770232,0.004219114,0.0001879642,0.0004590527],"study_design_scores_gemma":[0.0005763034,0.0007732662,0.06888787,0.00001526397,0.000125897,0.000003666267,0.00014891,0.9233211,0.002309041,0.003458003,0.0002038471,0.0001768555],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9913303,0.00002882409,0.0003989657,0.003337195,0.00002059288,0.000188555,0.00001289417,0.000008582412,0.004674151],"genre_scores_gemma":[0.9975663,0.0001121166,0.0002263885,0.002057205,0.00001576398,0.00001146604,0.000001502854,0.000005799018,0.000003386514],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8832317,"threshold_uncertainty_score":0.2373044,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04560056788755366,"score_gpt":0.2787208791225643,"score_spread":0.2331203112350106,"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."}}