{"id":"W3037253574","doi":"10.1139/cjfas-2020-0105","title":"Species-specific preferences drive the differential effects of lake factors on fish production","year":2020,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Natural Resources and Forestry; Canada Research Chairs; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto Mississauga; University of Toronto; Ontario Ministry of Natural Resources and Forestry; Ministry of Natural Resources","keywords":"Trout; Micropterus; Fishery; Ecology; Habitat; Salvelinus; Population; Hypolimnion; Environmental science; Forage fish; Population dynamics of fisheries; Biology; Bass (fish); Fish <Actinopterygii>; Nutrient","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"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":[0.0004512371,0.0002404009,0.0002324551,0.0006218665,0.0006048889,0.0007056607,0.0002646281,0.0002077167,0.002264356],"category_scores_gemma":[0.001611453,0.0002098947,0.0004290747,0.0008820918,0.000523147,0.0003458165,0.0005949501,0.000215323,0.0002017778],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001708311,"about_ca_system_score_gemma":0.001360761,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.5089181,"about_ca_topic_score_gemma":0.8383142,"domain_scores_codex":[0.9996555,0.00008246728,0.00001851456,0.0001066047,0.00005871461,0.00007827864],"domain_scores_gemma":[0.9990709,0.0002577211,0.0002646041,0.00009987153,0.0001508185,0.0001561033],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000489243,0.000006238734,0.9946561,0.0000132657,0.0001141388,0.00003308868,0.0003061384,0.0003330462,0.001777832,0.00007615796,0.0001883918,0.002446713],"study_design_scores_gemma":[0.00000100371,0.000003780204,0.9992664,0.000001938606,0.00001048033,0.000008860149,0.0001392325,0.0003334355,0.00005052159,0.00003948145,0.0001428281,0.000001946944],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9980008,0.0001139356,0.0003148897,0.00005486604,0.000001666251,0.000004399444,0.0006705959,0.000005998051,0.0008329654],"genre_scores_gemma":[0.9990393,0.00005427239,0.0002087049,0.00001534067,9.490571e-7,0.000003311458,0.0003995531,0.000004231914,0.0002743651],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5089181,"threshold_uncertainty_score":0.9879479,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0220923336916118,"score_gpt":0.1873373131522366,"score_spread":0.1652449794606249,"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."}}