{"id":"W2150709601","doi":"10.1111/faf.12013","title":"Primary productivity demands of global fishing fleets","year":2013,"lang":"en","type":"article","venue":"Fish and Fisheries","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":76,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"University of British Columbia","keywords":"Overfishing; Fishing; Marine ecosystem; Fishery; Productivity; Context (archaeology); Business; Sustainability; Natural resource economics; Ecosystem; Environmental science; Geography; Ecology; Economics","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001382898,0.0001161994,0.0001790008,0.00000854456,0.0001060221,0.00008623415,0.0001720359,0.00006529339,0.006911227],"category_scores_gemma":[0.0001054292,0.0001014712,0.00003395188,0.0001904959,0.0004140289,0.0007831998,0.0004471953,0.0001040179,0.00002950059],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000475489,"about_ca_system_score_gemma":0.0000135098,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002005166,"about_ca_topic_score_gemma":0.0005460658,"domain_scores_codex":[0.9989868,0.00003453946,0.0001713017,0.0002710742,0.00026334,0.0002729692],"domain_scores_gemma":[0.999567,0.00003203341,0.00005174336,0.0002233482,0.00001733492,0.0001085911],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001601403,0.00003780908,0.8716895,0.00003895449,0.000007409675,0.000001899245,0.0001300283,4.753428e-7,0.0002890667,0.00002440813,0.05372854,0.07403588],"study_design_scores_gemma":[0.0001382732,0.00008573654,0.9108015,0.000003369954,0.00000399084,0.000008540714,0.00008064193,0.00003905308,0.0001914349,0.0009547897,0.0875717,0.0001210077],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.660262,0.000006281717,0.00002172998,0.002156841,0.00004126572,0.0001535933,0.00001095276,0.00002217506,0.3373251],"genre_scores_gemma":[0.9956713,0.00003786556,0.0007041754,0.0004957023,0.00005600486,0.00003853841,0.00001299465,0.000008767575,0.00297466],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3354093,"threshold_uncertainty_score":0.9939966,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009480988202227356,"score_gpt":0.2063888230086753,"score_spread":0.196907834806448,"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."}}