{"id":"W2800578035","doi":"10.1111/faf.12285","title":"Empowering high seas governance with satellite vessel tracking data","year":2018,"lang":"en","type":"article","venue":"Fish and Fisheries","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":126,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Strong","keywords":"International waters; Business; Treaty; Sustainability; Overfishing; Corporate governance; Jurisdiction; Environmental resource management; Marine conservation; Marine protected area; United Nations Convention on the Law of the Sea; Fishery; Fishing; International law; Political science; Finance; Economics; Ecology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01172683,0.0002879045,0.0002688094,0.002520905,0.0007955513,0.003695091,0.0009874448,0.0006500749,0.004108056],"category_scores_gemma":[0.02703359,0.0003468647,0.0002660116,0.003006365,0.0008769772,0.004665355,0.004809406,0.0009822404,0.001308958],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001004665,"about_ca_system_score_gemma":0.002169313,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01331943,"about_ca_topic_score_gemma":0.01747692,"domain_scores_codex":[0.9941848,0.003404367,0.0003956515,0.0006548834,0.001017196,0.0003431794],"domain_scores_gemma":[0.9724135,0.01126026,0.004608379,0.006399565,0.004145335,0.001172884],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002589178,0.0003912224,0.3208395,0.0002344405,0.0001175861,0.0002129306,0.004211661,0.03829253,0.004016147,0.03609264,0.0223055,0.5730269],"study_design_scores_gemma":[0.0002261405,0.0007941254,0.2142732,0.001202549,0.0002259646,0.0002141217,0.01420555,0.378274,0.01540921,0.09714021,0.2777456,0.0002893959],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5160156,0.0006527988,0.2386744,0.01427764,0.0003592661,0.001102018,0.006556957,0.004288985,0.2180724],"genre_scores_gemma":[0.919961,0.0003556396,0.0721216,0.000436696,0.0001257564,0.0002942454,0.002351439,0.00007856484,0.00427512],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01331943,"threshold_uncertainty_score":0.06201816,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02460147375307551,"score_gpt":0.2555396115533084,"score_spread":0.2309381378002329,"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."}}