{"id":"W1981584192","doi":"10.1080/02755947.2011.578527","title":"Testing the Severity of Ill Effects Model for Predicting Fish Abundance and Condition","year":2011,"lang":"en","type":"article","venue":"North American Journal of Fisheries Management","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Fontinalis; Salvelinus; STREAMS; Trout; Sculpin; Environmental science; Index of biological integrity; Fish <Actinopterygii>; Cottus; Sediment; Habitat; Fishery; Stressor; Ecology; Biology; Computer science","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":[0.007925225,0.001125999,0.000653829,0.001069211,0.0003860888,0.000756128,0.001139244,0.00070432,0.001725213],"category_scores_gemma":[0.01393265,0.0003794587,0.00129545,0.0003438855,0.0006240422,0.0007318684,0.0009976184,0.0008932682,0.0002400054],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008492254,"about_ca_system_score_gemma":0.001096979,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01260726,"about_ca_topic_score_gemma":0.009287364,"domain_scores_codex":[0.9970469,0.001975429,0.0001332677,0.0003937196,0.0002149786,0.0002357039],"domain_scores_gemma":[0.9804435,0.01598051,0.001321334,0.0005600803,0.0009728951,0.0007216995],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001452451,0.0005357135,0.6530393,0.00002809638,0.0005584047,0.0001240044,0.00006858689,0.3299013,0.0007167901,0.0009062696,0.0007001315,0.01196891],"study_design_scores_gemma":[0.0000292963,0.0004052853,0.02380166,0.000004909341,0.00004362393,0.00003126284,0.0000296022,0.9748742,0.0001616513,0.0005280024,0.00007914423,0.0000113593],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9729087,0.00008990496,0.02554559,0.0002743774,0.00003250954,0.00003891138,0.0003360181,0.0001604352,0.0006135873],"genre_scores_gemma":[0.9969132,0.00001274562,0.002601219,0.00002363371,0.00001411691,0.00001901354,0.0001901139,0.00000601208,0.0002198441],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01260726,"threshold_uncertainty_score":0.04191309,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01576311270404956,"score_gpt":0.2042781659746277,"score_spread":0.1885150532705781,"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."}}