{"id":"W4388480727","doi":"10.1111/ecog.06801","title":"Globally coordinated acoustic aquatic animal tracking reveals unexpected, ecologically important movements across oceans, lakes and rivers","year":2023,"lang":"en","type":"article","venue":"Ecography","topic":"Marine animal studies overview","field":"Environmental Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; University of Waterloo; Kintama (Canada); Ocean Tracking Network; Dalhousie University","funders":"Bureau of Ocean Energy Management; NSW Department of Primary Industries; Norges Forskningsråd; Nature Conservancy; U.S. Department of the Interior","keywords":"Habitat; Ecology; Telemetry; Resource (disambiguation); Aquatic ecosystem; Biotelemetry; Freshwater ecosystem; Citizen science; Tracking (education); Ecosystem; Marine habitats; Geography; Environmental resource management; Environmental science; Biology; Computer science; Telecommunications","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":[0.001504856,0.0002436613,0.0002159181,0.002302361,0.0008151837,0.001319823,0.0004162159,0.0005839298,0.001937082],"category_scores_gemma":[0.001873831,0.000155779,0.0002166477,0.002825998,0.001183961,0.001182983,0.001845143,0.0005996516,0.0003917629],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000602578,"about_ca_system_score_gemma":0.000711165,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01372195,"about_ca_topic_score_gemma":0.06279509,"domain_scores_codex":[0.9992875,0.0001781329,0.00004655266,0.0002237989,0.0001678267,0.0000961724],"domain_scores_gemma":[0.9975398,0.0007459938,0.0007388724,0.0003604027,0.0003809731,0.0002340676],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008953095,0.00004200139,0.6659629,0.0009006331,0.0002047095,0.002025016,0.01481772,0.001007745,0.0157917,0.004849763,0.01115759,0.2831507],"study_design_scores_gemma":[0.000003402171,0.0001439491,0.9132299,0.0004718983,0.000107404,0.001232107,0.01614419,0.0003707795,0.001258859,0.002798748,0.06420536,0.00003337136],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8612595,0.009384067,0.02489968,0.003993088,0.0002511419,0.0001467389,0.003960019,0.0002834394,0.09582231],"genre_scores_gemma":[0.9667228,0.00779491,0.01525778,0.0008934914,0.0001724833,0.00007605425,0.001907561,0.00007774935,0.007097062],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01372195,"threshold_uncertainty_score":0.02728415,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01590721254559352,"score_gpt":0.2568690743936849,"score_spread":0.2409618618480914,"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."}}