{"id":"W4296736802","doi":"10.3389/fmars.2022.771547","title":"Informing Management of Atlantic Bluefin Tuna Using Telemetry Data","year":2022,"lang":"en","type":"article","venue":"Frontiers in Marine Science","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Acadia University","funders":"Natural Sciences and Engineering Research Council of Canada; U.S. Navy; National Oceanic and Atmospheric Administration; Acadia University","keywords":"Tuna; Fishery; Thunnus; Telemetry; Fishing; Population; Environmental science; Satellite; Mortality rate; Mark and recapture; Geography; Biology; Fish <Actinopterygii>; Computer science; Engineering; Demography; Telecommunications","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001445454,0.0002950142,0.0001557431,0.0004336854,0.0001735537,0.0005518608,0.0004295146,0.0004092419,0.0005852258],"category_scores_gemma":[0.005307584,0.0002061689,0.0001820415,0.0002474942,0.0001961954,0.0007625842,0.000355613,0.0003194188,0.0001403342],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008152968,"about_ca_system_score_gemma":0.0009950788,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04072646,"about_ca_topic_score_gemma":0.09484216,"domain_scores_codex":[0.9995816,0.0002361087,0.00002014097,0.00006497803,0.00005848383,0.00003863121],"domain_scores_gemma":[0.9975001,0.001138226,0.0008788064,0.0001668336,0.0002333482,0.00008271828],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001267669,0.0001460643,0.6055779,0.0000724913,0.0001265123,0.0001136684,0.0001855908,0.3152914,0.004684146,0.000961188,0.0008567408,0.07185762],"study_design_scores_gemma":[0.00003186385,0.0001193731,0.116799,0.00004241969,0.00004523302,0.00005058655,0.0001697508,0.8784931,0.001750881,0.00112562,0.001347056,0.00002509942],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9473006,0.0002491789,0.04882542,0.0009922715,0.00001844172,0.00004376458,0.0008475111,0.0001723925,0.00155038],"genre_scores_gemma":[0.9876903,0.0001224322,0.01156649,0.00004159955,0.000008634613,0.00002274677,0.0002830637,0.000004266652,0.0002604938],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04072646,"threshold_uncertainty_score":0.08097881,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02619384099416258,"score_gpt":0.2707074774893985,"score_spread":0.2445136364952359,"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."}}