{"id":"W4389376892","doi":"10.1139/cjfas-2023-0145","title":"Electronic tagging and tracking aquatic animals to understand a world increasingly shaped by a changing climate and extreme weather events","year":2023,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Marine animal studies overview","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; Ocean Tracking Network; Dalhousie University","funders":"LifeWatch – Niclas Öberg Foundation; European Cooperation in Science and Technology; Ministerio de Ciencia e Innovación; HORIZON EUROPE Framework Programme; Fonds Wetenschappelijk Onderzoek; Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement; Norges Forskningsråd; Vlaamse regering; European Commission","keywords":"Extreme weather; Climate change; Environmental science; Ecology; Meteorology; Climatology; Environmental resource management; Geography; Biology; Geology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.004153898,0.0004464711,0.0004588648,0.001867234,0.0008147412,0.001413831,0.0007504756,0.0009648412,0.003898813],"category_scores_gemma":[0.004335168,0.0002049778,0.0004899508,0.002125842,0.001156162,0.002573711,0.001498208,0.00105321,0.0005678753],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001096966,"about_ca_system_score_gemma":0.002551882,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04337109,"about_ca_topic_score_gemma":0.1129645,"domain_scores_codex":[0.9988894,0.0004654146,0.00007344011,0.0001830746,0.0002861193,0.0001025673],"domain_scores_gemma":[0.996226,0.001895544,0.0004780958,0.0003037849,0.0009451792,0.0001513444],"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":[0.0001068065,0.00009356625,0.0635483,0.005618728,0.0002516377,0.0002242516,0.002534335,0.001727195,0.009685312,0.01707703,0.01778955,0.8813433],"study_design_scores_gemma":[0.00002612885,0.0005053338,0.2221758,0.008991156,0.0006111342,0.00091018,0.007191752,0.001502203,0.004326128,0.02071185,0.7328955,0.0001528069],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.1312094,0.4462425,0.1900544,0.03542386,0.006640406,0.001011487,0.005430506,0.0006363853,0.1833509],"genre_scores_gemma":[0.2925903,0.4909676,0.1741814,0.01124197,0.002025147,0.0008429686,0.003043345,0.0000968796,0.02501045],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04337109,"threshold_uncertainty_score":0.08623725,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03853200621571713,"score_gpt":0.2388985266006426,"score_spread":0.2003665203849255,"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."}}