{"id":"W2761417033","doi":"10.2166/wqrj.2017.014","title":"SWAT modeling of hydrology, sediment and nutrients from the Grand River, Ontario","year":2017,"lang":"en","type":"article","venue":"Water Quality Research Journal","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Hydrology (agriculture); Environmental science; Soil and Water Assessment Tool; SWAT model; Sediment; Watershed; Nutrient; Surface runoff; Sediment transport; Streamflow; Drainage basin; Geology; Ecology; Geography; Geomorphology","routes":{"ca_aff":true,"ca_fund":false,"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":["sts"],"consensus_categories":[],"category_scores_codex":[0.004963037,0.00009640085,0.0001802999,0.0000287034,0.002215928,0.0001149321,0.0006305328,0.00005850308,0.000501472],"category_scores_gemma":[0.00007816255,0.00004991367,0.00006135274,0.00001783932,0.001525878,0.0002859186,0.001440905,0.0005674643,0.00007858595],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001010455,"about_ca_system_score_gemma":0.000009064574,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05832881,"about_ca_topic_score_gemma":0.01266931,"domain_scores_codex":[0.9979554,0.0005169701,0.0002752756,0.000227492,0.0005764216,0.0004484519],"domain_scores_gemma":[0.9993081,0.00009200574,0.00008210554,0.0003898022,0.00002689535,0.0001010674],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003377035,0.000119524,0.9733845,0.000007891967,0.0002106875,0.00002747416,0.01996698,0.001047421,0.001239988,0.0000603905,0.002826657,0.0007707874],"study_design_scores_gemma":[0.004750172,0.0004778002,0.7481825,0.0000725634,0.0001069166,0.00002573877,0.001044478,0.004136629,0.004410169,0.2075928,0.02880762,0.0003926944],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9893909,0.00006990886,0.0001670536,0.007796502,0.0001195433,0.0001517311,0.000003674352,0.000003404187,0.002297305],"genre_scores_gemma":[0.9985421,0.0002004347,0.0001196349,0.0001301023,0.00005847131,0.000005670071,0.000002217587,0.000004464287,0.0009368653],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.225202,"threshold_uncertainty_score":0.999083,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1207915832579789,"score_gpt":0.3595512876366818,"score_spread":0.2387597043787029,"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."}}