{"id":"W2810633417","doi":"10.1139/cjfas-2018-0016","title":"Bayesian inference from the conditional genetic stock identification model","year":2018,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":139,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Oceanic and Atmospheric Administration; National Science Foundation","keywords":"Inference; Computer science; Bayesian probability; Bayesian inference; Identification (biology); Econometrics; Parametric model; Machine learning; Artificial intelligence; Data mining; Statistics; Parametric statistics; Mathematics; Ecology; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002951355,0.00005488721,0.00007020815,0.00002686762,0.0008243453,0.0001093613,0.0002709859,0.00001957103,0.000999434],"category_scores_gemma":[0.0001710013,0.00003638198,0.00001598741,0.0001287959,0.00221696,0.0002708865,0.00002958267,0.00004966253,0.0000203123],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002794721,"about_ca_system_score_gemma":0.0001064945,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003174876,"about_ca_topic_score_gemma":0.1437965,"domain_scores_codex":[0.9994056,0.00002829517,0.0001696567,0.0001074554,0.0001447798,0.0001442354],"domain_scores_gemma":[0.9995961,0.0001047129,0.0001204382,0.0000645804,0.00001120579,0.0001029564],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000002503234,0.000004925016,0.9307074,9.375954e-7,0.00001558345,0.00000398066,0.001877704,0.0003642,0.00003165165,0.0005136887,0.06284927,0.003628182],"study_design_scores_gemma":[0.00007043005,0.00009632736,0.9204109,0.000007787618,0.00001794658,0.000005361434,0.000726991,0.02929172,0.00001358708,0.04649147,0.0028035,0.00006398378],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9664224,0.00007479917,0.0182514,0.01042102,0.0002242489,0.00007486305,0.000007301811,0.000002251995,0.004521656],"genre_scores_gemma":[0.9968016,0.00002260505,0.001463538,0.001437046,0.00005581528,0.000002114241,6.525752e-7,0.000001549908,0.0002151197],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1406216,"threshold_uncertainty_score":0.9999138,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02163058724168039,"score_gpt":0.2277888176721816,"score_spread":0.2061582304305012,"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."}}