{"id":"W2030402249","doi":"10.1139/f02-108","title":"Reference priors for Bayesian fisheries models","year":2002,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Economic and Environmental Valuation","field":"Economics, Econometrics and Finance","cited_by":57,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Prior probability; Bayesian probability; Econometrics; Population; Bayesian inference; Consistency (knowledge bases); Computer science; Posterior predictive distribution; Statistics; Mathematics; Artificial intelligence; Bayesian linear regression","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":[0.01470254,0.001130934,0.001512921,0.002766308,0.001145113,0.003465493,0.003167373,0.003628433,0.007088611],"category_scores_gemma":[0.06992482,0.001025887,0.001471738,0.002666218,0.002802503,0.007058349,0.002125771,0.005136852,0.002050635],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002750351,"about_ca_system_score_gemma":0.001591181,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004178875,"about_ca_topic_score_gemma":0.003407252,"domain_scores_codex":[0.993792,0.003764421,0.0003090704,0.0007074192,0.00121323,0.0002138786],"domain_scores_gemma":[0.9789364,0.01562062,0.001316225,0.002053239,0.001763544,0.0003099429],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001460916,0.00001064035,0.000254598,0.00005203236,0.00002242935,0.00005214314,0.0001346004,0.06714478,0.000120568,0.9125001,0.002129384,0.01756411],"study_design_scores_gemma":[0.000005630505,0.000005262753,0.00009926262,0.00004571646,0.00000716131,0.00003054462,0.00001987379,0.08631359,0.00008661886,0.9097461,0.003624148,0.00001596884],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001921828,0.0004638182,0.9920248,0.0005773918,0.00003694713,0.0000215588,0.0001826686,0.0001655374,0.0046054],"genre_scores_gemma":[0.3022159,0.003923899,0.6797721,0.001042404,0.0005446633,0.0006950434,0.001690008,0.0005400272,0.009575851],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01470254,"threshold_uncertainty_score":0.07775539,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1672973925148585,"score_gpt":0.2075451816496231,"score_spread":0.04024778913476465,"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."}}