{"id":"W2040342851","doi":"10.1139/f06-022","title":"Integrating design- and model-based inference to estimate length and age composition in North Pacific longline catches","year":2006,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"School of Aquatic and Fishery Sciences","keywords":"Groundfish; Gadus; Stock assessment; Statistics; Sampling (signal processing); Fishery; Sample size determination; Sampling design; Sebastes; Multinomial distribution; Econometrics; Mathematics; Fisheries management; Biology; Geography; Fishing; Fish <Actinopterygii>; Computer science","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":[0.05230894,0.0007078808,0.001049708,0.001267143,0.0004456496,0.001595946,0.001247683,0.001280124,0.0006041995],"category_scores_gemma":[0.1493468,0.001136228,0.001657914,0.0009104541,0.001168644,0.001568163,0.001348469,0.001055711,0.0001246553],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00174501,"about_ca_system_score_gemma":0.002782358,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01321027,"about_ca_topic_score_gemma":0.01471565,"domain_scores_codex":[0.9794048,0.01614295,0.0007782077,0.001790814,0.001579109,0.0003041193],"domain_scores_gemma":[0.8789354,0.1000392,0.008441448,0.008493751,0.003628827,0.0004612392],"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":[0.000415248,0.0003829119,0.1353655,0.0002188656,0.001640088,0.000118803,0.0003774702,0.7232947,0.001456722,0.009158442,0.0003165708,0.1272547],"study_design_scores_gemma":[0.00007562467,0.0001643537,0.01088547,0.00001722364,0.0001581845,0.00002581393,0.00002061077,0.9756517,0.0006631002,0.0120456,0.0002668012,0.00002543096],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1991606,0.0002162479,0.799005,0.0001700616,0.00002996845,0.0001783822,0.0001103749,0.0003730465,0.0007563452],"genre_scores_gemma":[0.8021907,0.0001239556,0.1966816,0.000103556,0.00002674633,0.0002618548,0.0002315829,0.00005238794,0.0003276174],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05230894,"threshold_uncertainty_score":0.2766394,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02627398077958407,"score_gpt":0.2532480268513252,"score_spread":0.2269740460717412,"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."}}