{"id":"W174655586","doi":"","title":"Watershed Assessment of the Canaseraga Creek Watershed, Including Water Quality Analysis, SWAT Model, and Investigation of the Applicability of a Nutrient Biotic Index","year":2013,"lang":"en","type":"dissertation","venue":"SUNY Digital Repository Support (State University of New York System)","topic":"Soil and Water Nutrient Dynamics","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Watershed; Environmental science; Water quality; Nutrient; Hydrology (agriculture); Soil and Water Assessment Tool; Index (typography); SWAT model; Water resource management; Geography; Ecology; Engineering; Computer science; Cartography; Geotechnical engineering; Streamflow; Drainage basin; Biology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003436211,0.0003057705,0.0007962772,0.0001266672,0.0002046281,0.00004604259,0.0006847008,0.0002071778,0.00001172779],"category_scores_gemma":[0.000008860334,0.0002063788,0.0004300156,0.0004449383,0.0005754839,0.0003390921,0.0004450429,0.0001933593,0.00000102792],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004886325,"about_ca_system_score_gemma":0.0001886394,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03403902,"about_ca_topic_score_gemma":0.001979276,"domain_scores_codex":[0.9970866,0.0001963528,0.0009681682,0.000539477,0.000924735,0.0002846663],"domain_scores_gemma":[0.9976137,0.00004512212,0.001240674,0.0008056195,0.0001277734,0.0001671351],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001062144,0.000085887,0.9794785,0.0009894795,0.0003467604,0.000002182054,0.006206646,0.006609932,0.005953608,0.00001238451,0.0001433389,0.00006504],"study_design_scores_gemma":[0.0009388814,0.0001424016,0.9370039,0.0003711949,0.001023834,0.000005095946,0.01577559,0.01075761,0.032709,0.0007153755,0.0000751483,0.0004819465],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9971743,0.000007186154,0.0001585779,0.00002164464,0.0001720973,0.0008064198,0.0001545392,0.00001556689,0.001489713],"genre_scores_gemma":[0.9943687,0.000003722405,0.00005937843,0.000003982673,0.000006213809,0.000001678141,0.0003429952,0.00001635944,0.005197024],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0424746,"threshold_uncertainty_score":0.9723934,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0146424824680479,"score_gpt":0.2126192689516118,"score_spread":0.1979767864835639,"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."}}