{"id":"W2015952654","doi":"10.13031/2013.17662","title":"Application of SWAT to Meet Water Quality Requirements for Canadian Conditions-A Study in Grand River Watershed","year":2004,"lang":"en","type":"article","venue":"2004, Ottawa, Canada August 1 - 4, 2004","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Watershed; Soil and Water Assessment Tool; Tributary; SWAT model; Environmental science; Hydrology (agriculture); Watershed management; Water quality; Drainage basin; Water resource management; Sediment; Streamflow; Geography; Geology; Computer science; Ecology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003316503,0.0003836895,0.0002649972,0.0003188909,0.001390639,0.000740651,0.000773856,0.0003685645,0.0009623762],"category_scores_gemma":[0.0009804248,0.0002148586,0.0003630942,0.0008606316,0.0004117237,0.0004257529,0.0002434848,0.0003372925,0.00009101218],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01089749,"about_ca_system_score_gemma":0.008616423,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9695044,"about_ca_topic_score_gemma":0.9832079,"domain_scores_codex":[0.9998185,0.0000168929,0.000006640751,0.00003661477,0.00006733364,0.0000539541],"domain_scores_gemma":[0.9996817,0.000062135,0.00002050316,0.0000206677,0.0001634883,0.00005145056],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0004626621,0.0006806824,0.3156067,0.0001415928,0.0001050179,0.001136315,0.0008544529,0.5906757,0.02957096,0.002585528,0.004765769,0.05341471],"study_design_scores_gemma":[0.0001063735,0.0002233737,0.2398873,0.00001114655,0.00006783002,0.0001241511,0.001404147,0.7432584,0.01002437,0.0004847698,0.004337596,0.000070603],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9969565,0.00002496366,0.0007822469,0.0000851903,0.000002691107,0.00003742735,0.0004097148,0.00008122074,0.001620145],"genre_scores_gemma":[0.9954359,0.00007006276,0.002783494,0.00002154161,0.000001277429,0.00001928572,0.0006631851,0.00001902295,0.0009861996],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03049558,"threshold_uncertainty_score":0.07906717,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01512457386269104,"score_gpt":0.2583508475663621,"score_spread":0.2432262737036711,"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."}}