{"id":"W4200153728","doi":"10.1016/j.scitotenv.2021.152301","title":"Improving monitoring of fish health in the oil sands region using regularization techniques and water quality variables","year":2021,"lang":"en","type":"article","venue":"The Science of The Total Environment","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Alberta Environment and Protected Areas; University of Guelph","funders":"","keywords":"Perch; Environmental science; Trout; Water quality; Regression analysis; Fishery; Stepwise regression; Statistics; Fish <Actinopterygii>; Biology; Mathematics; Ecology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000634435,0.0003208915,0.0002925757,0.0005440515,0.0001931592,0.000321122,0.0003636026,0.0004779415,0.0001628],"category_scores_gemma":[0.001010919,0.0001471625,0.0002570665,0.0003744526,0.0002096849,0.0003444866,0.0003175085,0.0002911815,0.00004272774],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003346507,"about_ca_system_score_gemma":0.0006293191,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02727749,"about_ca_topic_score_gemma":0.06190398,"domain_scores_codex":[0.9997888,0.00006629522,0.0000100497,0.00006013054,0.00004172265,0.00003308897],"domain_scores_gemma":[0.9995819,0.0001421915,0.0001176892,0.00002609171,0.00009683853,0.00003526454],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000852145,0.000538016,0.6338675,0.0001086757,0.0003861037,0.0001219056,0.0002566426,0.1051226,0.1421994,0.0003465409,0.0006208493,0.1155796],"study_design_scores_gemma":[0.00003736903,0.0001951903,0.5437765,0.00001062816,0.0001155608,0.00005200531,0.0001799596,0.4428207,0.01234616,0.0002099283,0.0002286339,0.00002742296],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9937299,0.00003679796,0.005930285,0.00004522917,0.000002398682,0.00000497459,0.0000537615,0.00002812401,0.000168426],"genre_scores_gemma":[0.9925165,0.00002898177,0.00711074,0.000008826439,0.000004321581,0.00000473173,0.00009983993,0.000003418603,0.0002227004],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9727225,"threshold_uncertainty_score":0.05423743,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01849851287999985,"score_gpt":0.2443602920924796,"score_spread":0.2258617792124797,"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."}}