{"id":"W2023693013","doi":"10.1139/cjfas-2014-0347","title":"Catch estimation in the federal trawl fisheries off Alaska: a simulation approach to compare the statistical properties of three trip-specific catch estimators","year":2015,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Oceanic and Atmospheric Administration","keywords":"Estimator; Statistics; Ratio estimator; Sampling (signal processing); Stock assessment; Fisheries management; Fishery; Econometrics; Variance (accounting); Computer science; Minimum-variance unbiased estimator; Environmental science; Mathematics; Bias of an estimator; Biology; Fishing; Economics","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":[0.009456886,0.00061878,0.0007691911,0.001321049,0.0006580019,0.0008988325,0.00112234,0.001393524,0.0009021999],"category_scores_gemma":[0.02308856,0.0005638502,0.001585607,0.001215435,0.0006070064,0.001211152,0.0008167537,0.001284505,0.00008473247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002222546,"about_ca_system_score_gemma":0.001685518,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06282371,"about_ca_topic_score_gemma":0.05087734,"domain_scores_codex":[0.9980474,0.001334937,0.0001363646,0.0001995146,0.0001372795,0.0001445849],"domain_scores_gemma":[0.9617756,0.03336974,0.001768729,0.001057616,0.001735905,0.0002923666],"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":[0.0003371123,0.0002422546,0.04329216,0.00005486894,0.0002396823,0.00009232783,0.0001717568,0.9470004,0.0004595825,0.002197851,0.0001980017,0.005714041],"study_design_scores_gemma":[0.0000380893,0.0002139427,0.00799126,0.00001725446,0.0000684808,0.00002223535,0.0001037719,0.99038,0.0003497719,0.0006849635,0.0001056818,0.00002451081],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9791626,0.0001118708,0.01945368,0.00007971108,0.000009885935,0.00008120608,0.000250574,0.00006351637,0.0007869161],"genre_scores_gemma":[0.9835164,0.00007814854,0.01552522,0.00002356197,0.000003377761,0.000160009,0.0003385962,0.00001342759,0.0003412067],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06282371,"threshold_uncertainty_score":0.1249161,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08265940682531242,"score_gpt":0.2665138367223646,"score_spread":0.1838544298970521,"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."}}