{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005280274,0.0002712244,0.0003235226,0.00005731381,0.0003970821,0.00009594228,0.0003406031,0.00007395374,0.0006342238],"category_scores_gemma":[0.0001624301,0.000242314,0.0001321024,0.001418306,0.0002937275,0.0002049409,0.0006956971,0.0001474641,0.0002366598],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008008826,"about_ca_system_score_gemma":0.000005409473,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000526579,"about_ca_topic_score_gemma":0.00378101,"domain_scores_codex":[0.9978439,0.0000670363,0.0004259962,0.000581777,0.0003836592,0.0006976571],"domain_scores_gemma":[0.9993117,0.0001193258,0.0001817968,0.0002076708,0.00001406105,0.0001654344],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003396647,0.00007460299,0.9853058,0.00002570854,0.00008468003,0.0001219514,0.0002599547,0.00004739574,0.003553996,0.00002690122,0.00666063,0.003804404],"study_design_scores_gemma":[0.000484493,0.0003119629,0.9944302,0.00002508104,0.00003149802,0.000005345127,0.0004637725,0.0007856182,0.00002172352,0.001030356,0.002108331,0.0003016251],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.995046,0.0002784518,0.00001130384,0.0001615022,0.0000978041,0.000390357,0.00006029601,0.0002486172,0.003705634],"genre_scores_gemma":[0.9981283,0.000859577,0.0001976248,0.0005124427,0.00002145335,0.00002676,0.00002196571,0.00002015841,0.0002116979],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009124384,"threshold_uncertainty_score":0.9881278,"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."}}