{"id":"W2101633265","doi":"10.1016/j.icesjms.2006.07.002","title":"Variation in the catchability of yellow perch (Perca flavescens) in the fisheries of Lake Erie using a Bayesian error-in-variable approach","year":2006,"lang":"en","type":"article","venue":"ICES Journal of Marine Science","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Ontario Commercial Fisheries' Association","funders":"","keywords":"Fishery; Perch; Fishing; Catch per unit effort; Fisheries management; Biomass (ecology); Population; Geography; Environmental science; Abundance (ecology); Population size; Fish <Actinopterygii>; Ecology; Biology","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":[0.004816715,0.0001910021,0.000235648,0.0007351163,0.000172878,0.000659795,0.0004079608,0.0004133969,0.0003358494],"category_scores_gemma":[0.008667385,0.000268628,0.0003025246,0.0004437454,0.0004720452,0.0004589236,0.0006497236,0.0002284871,0.00006275016],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009499934,"about_ca_system_score_gemma":0.0003731473,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03592739,"about_ca_topic_score_gemma":0.0558543,"domain_scores_codex":[0.9986497,0.0007283078,0.00009977318,0.000225593,0.0001812286,0.0001155003],"domain_scores_gemma":[0.9964889,0.001913422,0.0006943645,0.000267086,0.0005339014,0.0001024079],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001873956,0.0000286551,0.9872807,0.00001087708,0.0002616947,0.00005175933,0.0002750694,0.005349009,0.001752509,0.0002561324,0.00007163156,0.004474632],"study_design_scores_gemma":[0.000008368019,0.00007366073,0.9678239,0.000007111443,0.00005071379,0.00004288131,0.0001711432,0.03127975,0.0002580366,0.0001406162,0.0001310915,0.00001273291],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9988488,0.00002081083,0.0009600883,0.000009717242,4.425019e-7,0.000002408905,0.00003898546,0.000004394682,0.0001143151],"genre_scores_gemma":[0.9995172,0.000007997238,0.0003217665,0.000004037672,3.579232e-7,0.000002771391,0.00008210175,0.000001800474,0.00006195095],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03592739,"threshold_uncertainty_score":0.07143652,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01331951670730668,"score_gpt":0.2325315519042243,"score_spread":0.2192120351969176,"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."}}