{"id":"W2887286417","doi":"10.1126/sciadv.aar3279","title":"Far from home: Distance patterns of global fishing fleets","year":2018,"lang":"en","type":"article","venue":"Science Advances","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":158,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Paul M. Angell Family Foundation; Marisla Foundation; MAVA Foundation; Oak Foundation; David and Lucile Packard Foundation","keywords":"Fishing; Geography; China; Fishery; Nautical mile; Exclusive economic zone; Sustainability; United Nations Convention on the Law of the Sea; Convention; Ecology; Political science","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0002576033,0.00008469683,0.0001057099,0.00001334566,0.0002282152,0.00006265548,0.000871214,0.00002005671,0.004155204],"category_scores_gemma":[0.0001195454,0.00006990506,0.0000242363,0.0007043178,0.001918219,0.001353874,0.0005021774,0.00005975136,0.0001159765],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001189748,"about_ca_system_score_gemma":0.00002737727,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001735281,"about_ca_topic_score_gemma":0.003546884,"domain_scores_codex":[0.9983094,0.00001636983,0.0001498113,0.0004072138,0.0007464616,0.000370738],"domain_scores_gemma":[0.9994357,0.00003252194,0.00006813983,0.0003251026,0.00002593847,0.000112633],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000109324,0.00001769059,0.8479369,0.000002810426,9.06793e-7,0.000002039388,0.0001304235,0.000008035212,0.005123858,0.000150265,0.0000701239,0.1465461],"study_design_scores_gemma":[0.0001688126,0.0001574512,0.8595293,0.00001382863,0.000002504752,0.000001572263,0.0004601205,0.0003504001,0.01478701,0.004537971,0.1197841,0.0002069562],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8934574,0.00003914519,0.002501815,0.0001704821,0.0002399622,0.00007227211,0.00004832178,0.00001987817,0.1034507],"genre_scores_gemma":[0.9977357,0.00003316284,0.001722568,0.0001004336,0.00005839376,0.000004091733,0.000001734119,0.000003072665,0.0003408357],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1463391,"threshold_uncertainty_score":0.9967551,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01047512313747115,"score_gpt":0.2809860943062038,"score_spread":0.2705109711687327,"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."}}