{"id":"W1994315542","doi":"10.1139/cjfas-2014-0405","title":"Comparing size-limit and quota policies to increase economic yield in a lobster fishery","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":"Australian Seafood Cooperative Research Centre","keywords":"Yield (engineering); Production (economics); Economics; Sustainability; Fishery; Fisheries management; Catch per unit effort; Environmental science; Agricultural economics; Statistics; Econometrics; Natural resource economics; Mathematics; Fishing; Ecology; Biology; Microeconomics","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":[0.007210491,0.0005917521,0.0006963328,0.000759266,0.0002862795,0.001076097,0.0007655209,0.001097776,0.001139307],"category_scores_gemma":[0.01544165,0.0002688479,0.0006401528,0.0004582871,0.0008783989,0.001934455,0.0008914635,0.0007606365,0.0001039969],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00303391,"about_ca_system_score_gemma":0.001854284,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008978162,"about_ca_topic_score_gemma":0.006290466,"domain_scores_codex":[0.9979244,0.001024485,0.0001646994,0.0002043682,0.0003548782,0.0003271372],"domain_scores_gemma":[0.9901685,0.007424566,0.001043889,0.0003442217,0.0006503725,0.0003684702],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.01061708,0.003077677,0.04596288,0.0007263026,0.000675122,0.0002360666,0.0003182423,0.8284049,0.0128103,0.01806807,0.0009416999,0.07816156],"study_design_scores_gemma":[0.002383247,0.03528263,0.1425059,0.0002756448,0.001201713,0.0001198853,0.001297868,0.7659223,0.02567181,0.01982266,0.005308508,0.0002079444],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9927907,0.0004387526,0.003307005,0.0002100683,0.00003163927,0.0001086015,0.00007162671,0.00003090216,0.003010707],"genre_scores_gemma":[0.9983046,0.0001501811,0.001062037,0.00006297717,0.000005839533,0.00004779381,0.00003750431,0.000004660968,0.0003244658],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008978162,"threshold_uncertainty_score":0.0381332,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.054128607566301,"score_gpt":0.2482339083502764,"score_spread":0.1941053007839754,"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."}}