{"id":"W6995858288","doi":"","title":"Predicting coastal cutthroat trout molt productive capacity from physiographic variables","year":2016,"lang":"en","type":"other","venue":"VIUSpace (Vancouver Island University Library)","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"California Department of Fish and Wildlife; Fisheries and Oceans Canada; Ministry of Environment; Washington Department of Fish and Wildlife; Royal Roads University; Ministry of Forests, Lands and Natural Resource Operations; Washington State University","keywords":"Fish migration; Abundance (ecology); Habitat; Trout; Watershed; Alewife; Channel (broadcasting); Oncorhynchus","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.0001920218,0.000306042,0.0001053351,0.0003816882,0.0001911025,0.0004463101,0.000178213,0.0001480869,0.00154672],"category_scores_gemma":[0.0006399616,0.0001952544,0.0001649556,0.0002745549,0.00009613275,0.0001963063,0.0002244444,0.000129345,0.0004768173],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007104608,"about_ca_system_score_gemma":0.001126932,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2083105,"about_ca_topic_score_gemma":0.4367859,"domain_scores_codex":[0.9999591,0.000008245941,0.000002080921,0.000012241,0.000008793318,0.00000958863],"domain_scores_gemma":[0.9998873,0.00003643779,0.00001702038,0.000007972742,0.0000290452,0.00002206298],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006341886,0.00006079467,0.9018953,0.00001379144,0.0000419411,0.00006033692,0.00005268781,0.07240485,0.001612076,0.0001032528,0.0007281542,0.02296334],"study_design_scores_gemma":[0.00001959798,0.00004218031,0.6204145,0.00001199396,0.00002315244,0.00003812426,0.0001754425,0.3766483,0.001590746,0.0002609851,0.000764674,0.00001035601],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9963118,0.00002280831,0.001674392,0.00002875881,0.000001511867,0.0000124713,0.0006686194,0.0000733423,0.001206167],"genre_scores_gemma":[0.9956158,0.00004076152,0.001886513,0.000005525359,0.000001575932,0.00001380189,0.001294798,0.000008995114,0.001132238],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2083105,"threshold_uncertainty_score":0.414196,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004724582598437178,"score_gpt":0.1584771837969983,"score_spread":0.1537526011985611,"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."}}