{"id":"W4390672990","doi":"10.1038/s41597-023-02824-6","title":"A database of mapped global fishing activity 1950–2017","year":2024,"lang":"en","type":"article","venue":"Scientific Data","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Fishing; Tonnage; Marine conservation; Computer science; Range (aeronautics); Database; Identification (biology); Automatic Identification System; Environmental science; Fishery; Environmental resource management; Oceanography; Data mining; Ecology; Engineering; Geology","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":["insufficient_payload"],"category_scores_codex":[0.001349519,0.00008311921,0.00009386506,0.00003234448,0.0001580441,0.0004782172,0.00173964,0.0000298093,0.009429246],"category_scores_gemma":[0.0002313076,0.00007149556,0.00002718045,0.000787507,0.0005688388,0.001453131,0.005321427,0.0001310961,0.0008080054],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006777435,"about_ca_system_score_gemma":0.00005849011,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001336429,"about_ca_topic_score_gemma":0.0014191,"domain_scores_codex":[0.9981203,0.00004461662,0.000128777,0.0007625868,0.0006517193,0.0002920177],"domain_scores_gemma":[0.9977463,0.0000465238,0.0000272568,0.00206477,0.000007018629,0.0001080634],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001744487,0.00008270747,0.01580198,0.00009815781,0.00001263939,0.00005802688,0.00009132097,0.000002409612,0.01019099,0.000150625,0.8042659,0.1692279],"study_design_scores_gemma":[0.00008350237,0.00001565278,0.009950654,0.00002440625,0.00001060558,0.00001026432,0.00005198536,0.01990092,0.0006620907,0.0003142517,0.9688464,0.0001292559],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3421097,0.0000907643,0.002844212,0.001977956,0.002772688,0.0004871102,0.02521566,0.0001627711,0.6243391],"genre_scores_gemma":[0.9777979,0.00002114004,0.001821461,0.00003120312,0.00007017395,0.000006615383,0.002645799,0.00001092739,0.01759476],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6356882,"threshold_uncertainty_score":0.99997,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08081955254806912,"score_gpt":0.3387792981972023,"score_spread":0.2579597456491332,"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."}}