{"id":"W2151077176","doi":"10.1139/cjfas-2014-0408","title":"Efficient statistical estimators and sampling strategies for estimating the age composition of fish","year":2015,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Estimator; Statistics; Gadus; Stock assessment; Fish <Actinopterygii>; Mathematics; Sampling (signal processing); Cluster sampling; Variance (accounting); Sample size determination; Fishery; Environmental science; Econometrics; Biology; Fishing; Computer science; Demography; Population","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":[0.0371295,0.00101912,0.0009166495,0.003377717,0.0004376144,0.001146586,0.001796952,0.001269624,0.001046189],"category_scores_gemma":[0.1262974,0.0009219662,0.0007702084,0.002262749,0.001388237,0.002307273,0.001174802,0.001173287,0.0004751056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008902564,"about_ca_system_score_gemma":0.001836242,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00277769,"about_ca_topic_score_gemma":0.003091162,"domain_scores_codex":[0.9813464,0.01476038,0.0007789931,0.001015049,0.001918621,0.0001806391],"domain_scores_gemma":[0.9174538,0.06401252,0.006359903,0.007291438,0.004578864,0.000303414],"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":[0.000168385,0.0002299606,0.0355932,0.0004818832,0.0005296743,0.0001264351,0.0004174613,0.4034594,0.005610069,0.1665827,0.002096856,0.384704],"study_design_scores_gemma":[0.0001567323,0.0003337647,0.01012984,0.0001610141,0.0001246325,0.000242726,0.0001385024,0.8762857,0.005753605,0.1026031,0.003984864,0.00008549486],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005445655,0.0001734418,0.9939224,0.00004755189,0.000007195672,0.00008676141,0.0000472794,0.00008073405,0.0001888957],"genre_scores_gemma":[0.08566116,0.0004356324,0.912265,0.00006881711,0.00004688545,0.0007767128,0.0003499954,0.00005422156,0.0003416655],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0371295,"threshold_uncertainty_score":0.196362,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05469572310580135,"score_gpt":0.2925348216056396,"score_spread":0.2378390984998383,"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."}}