{"id":"W3013528058","doi":"10.1111/faf.12458","title":"Tuna trade‐offs: Balancing profit and social benefits in one of the world’s largest fisheries","year":2020,"lang":"en","type":"article","venue":"Fish and Fisheries","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Social Sciences and Humanities Research Council of Canada; Mitacs","keywords":"Tuna; Subsidy; Fishing; Profit (economics); Fishery; Business; Fisheries management; Sustainability; Environmental economics; Natural resource economics; Environmental resource management; Economics; Ecology; Microeconomics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001615279,0.0004936368,0.0004179052,0.0006447143,0.0008619135,0.002311765,0.0007054015,0.001017634,0.003139399],"category_scores_gemma":[0.003216922,0.0002914348,0.0005512011,0.0004990962,0.0009570317,0.002028834,0.001357058,0.0007732482,0.0001459927],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003443961,"about_ca_system_score_gemma":0.001997228,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01680011,"about_ca_topic_score_gemma":0.0233496,"domain_scores_codex":[0.9993601,0.0003062384,0.0000168493,0.00008328221,0.00007067491,0.0001628464],"domain_scores_gemma":[0.9989265,0.0006280679,0.0001326954,0.00004842373,0.00009067509,0.0001737796],"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":[0.0007852133,0.0004932073,0.0237061,0.0002036639,0.0002394493,0.0006122693,0.0001847613,0.886645,0.008594168,0.04677653,0.001331483,0.03042822],"study_design_scores_gemma":[0.0001045048,0.0006822865,0.02116974,0.0001194571,0.0001457073,0.0001331017,0.001489952,0.9321657,0.003096144,0.03815238,0.002684335,0.00005663908],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9688007,0.0002346735,0.01277957,0.001192679,0.00002151424,0.00009012508,0.0001702813,0.00002826769,0.01668214],"genre_scores_gemma":[0.997613,0.00005033194,0.001689784,0.00003768452,0.000002863459,0.00001742509,0.00001630028,0.0000032292,0.0005694111],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01680011,"threshold_uncertainty_score":0.03340465,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02798354028277078,"score_gpt":0.2122312192927686,"score_spread":0.1842476790099978,"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."}}