{"id":"W1488833008","doi":"","title":"Water Quality Analysis of Black Creek Watershed: Identification of Point and Nonpoint Sources of Pollution and Loading Simulation Using the SWAT Model","year":2012,"lang":"en","type":"dissertation","venue":"SUNY Digital Repository Support (State University of New York System)","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Nonpoint source pollution; SWAT model; Watershed; Environmental science; Water quality; Identification (biology); Pollution; Soil and Water Assessment Tool; Water resource management; Hydrology (agriculture); Geography; Computer science; Engineering; Cartography; Drainage basin; Streamflow; Ecology; Geotechnical engineering","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":[0.0001925759,0.0004810847,0.0003706604,0.0005049348,0.0007121646,0.0007331501,0.0006816516,0.0005757578,0.001739011],"category_scores_gemma":[0.0004802831,0.0002859066,0.000579298,0.0007301331,0.000244206,0.000362489,0.0002939305,0.0002605029,0.0001994602],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003222242,"about_ca_system_score_gemma":0.003480048,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.6630431,"about_ca_topic_score_gemma":0.6384516,"domain_scores_codex":[0.9998857,0.00001509017,0.000006719763,0.00003202709,0.00003490431,0.00002555976],"domain_scores_gemma":[0.9997867,0.00004766293,0.00002332077,0.00001096759,0.0001053277,0.00002599874],"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":[0.000108822,0.0001492221,0.06662985,0.00003116426,0.00005791615,0.0001581161,0.0001121107,0.9157784,0.0037095,0.0006279752,0.001760246,0.0108766],"study_design_scores_gemma":[0.000009984823,0.00001251672,0.009056511,0.000001219057,0.000005722587,0.000003786878,0.0000339785,0.9901971,0.0003144382,0.00006899048,0.0002900998,0.000005664353],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9846728,0.00002262877,0.007935192,0.0001124299,0.000007388884,0.000070646,0.002445325,0.0004587355,0.004274852],"genre_scores_gemma":[0.9890485,0.00003654349,0.006073645,0.00001785644,0.000003024364,0.00004846067,0.002280937,0.00004095519,0.002449983],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6630431,"threshold_uncertainty_score":0.6778826,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02056923336054644,"score_gpt":0.2314985829760257,"score_spread":0.2109293496154792,"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."}}