{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01753825,0.0008394115,0.001638562,0.001777727,0.0009121899,0.002043458,0.003820126,0.001644986,0.006149095],"category_scores_gemma":[0.07995076,0.0008170335,0.001478856,0.001762407,0.001850509,0.002829588,0.00225024,0.003266541,0.001112782],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00183961,"about_ca_system_score_gemma":0.002619857,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01444149,"about_ca_topic_score_gemma":0.01330103,"domain_scores_codex":[0.9951844,0.002822644,0.0002493313,0.0008173801,0.0006395473,0.0002866588],"domain_scores_gemma":[0.9611332,0.03299956,0.001400164,0.002365156,0.001726088,0.0003758504],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00017075,0.00005356985,0.0122702,0.0001638021,0.0002061918,0.0002186731,0.0002126498,0.7315459,0.0008180681,0.1880001,0.00455837,0.0617818],"study_design_scores_gemma":[0.00001732375,0.00001199029,0.0009875835,0.00003668655,0.00002424033,0.00004630507,0.0000162091,0.8874595,0.0002916226,0.1103339,0.0007516565,0.00002288288],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02392702,0.0001801159,0.972573,0.0003949569,0.0000416091,0.00004569308,0.0007104602,0.0004443041,0.00168285],"genre_scores_gemma":[0.6014974,0.0005914329,0.3878096,0.0006236879,0.0002326433,0.00047921,0.00419658,0.0003685545,0.004201069],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01753825,"threshold_uncertainty_score":0.09275222,"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."}}