{"id":"W2783941813","doi":"10.1139/cjfas-2017-0152","title":"Fishing on floating objects (FOBs): how French tropical tuna purse seiners split fishing effort between GPS-monitored and unmonitored FOBs","year":2018,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institut de Recherche pour le Développement","keywords":"Fishing; Tuna; Fishery; Global Positioning System; Catch per unit effort; Fish <Actinopterygii>; Environmental science; Geography; Biology; 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":[0.0009673982,0.0001927153,0.0001930548,0.001066026,0.0006995999,0.001329901,0.0003395111,0.0003881566,0.001820019],"category_scores_gemma":[0.003538402,0.0001229363,0.0002353684,0.001025671,0.0007088313,0.0009762978,0.000746499,0.0002709115,0.00035513],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001227823,"about_ca_system_score_gemma":0.0005756653,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1358707,"about_ca_topic_score_gemma":0.2907982,"domain_scores_codex":[0.9993777,0.0001796726,0.00003390157,0.0001619377,0.0001121581,0.0001346779],"domain_scores_gemma":[0.9984109,0.0003737652,0.000662008,0.00009868683,0.0002376925,0.0002168634],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006904506,0.00002387447,0.9635251,0.00003211843,0.00007509588,0.0001490372,0.007085046,0.000278418,0.001307136,0.0003359861,0.0008445487,0.02627453],"study_design_scores_gemma":[8.989351e-7,0.00002540823,0.9947006,0.00001919552,0.00001234244,0.00005873872,0.003598259,0.0003237625,0.00006583025,0.0000757115,0.001110521,0.000008836638],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9967915,0.0002041259,0.0002193356,0.0001868984,0.000005175906,0.000004731224,0.0001417945,0.000007405281,0.002438912],"genre_scores_gemma":[0.9983929,0.0001720653,0.0002977769,0.00006961175,0.000004501119,0.000005082335,0.0001750422,0.000006970439,0.000875974],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1358707,"threshold_uncertainty_score":0.2701598,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02621261544610531,"score_gpt":0.2488869081196277,"score_spread":0.2226742926735224,"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."}}