{"id":"W2566090446","doi":"10.1002/ece3.2795","title":"A hidden Markov movement model for rapidly identifying behavioral states from animal tracks","year":2017,"lang":"en","type":"article","venue":"Ecology and Evolution","topic":"Animal Vocal Communication and Behavior","field":"Biochemistry, Genetics and Molecular Biology","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ocean Tracking Network; Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Killam Trusts; Dalhousie University; Great Lakes Fishery Commission","keywords":"Movement (music); Hidden Markov model; Computer science; Artificial intelligence; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001303424,0.00009126389,0.00009917859,0.00001741969,0.0004896173,0.00004150929,0.00018785,0.0001625327,0.0000201998],"category_scores_gemma":[0.00002625841,0.00009308005,0.00005219485,0.000006702357,0.0001224874,0.00001355623,0.0001556442,0.00006792549,0.000004098864],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002138451,"about_ca_system_score_gemma":0.00003115779,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001044696,"about_ca_topic_score_gemma":0.0008732663,"domain_scores_codex":[0.9994106,0.00002857404,0.0001354514,0.0002251871,0.0000396824,0.0001604467],"domain_scores_gemma":[0.9994962,0.000008797969,0.0001025503,0.0002903362,0.00005317558,0.00004893694],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0008704449,0.000352554,0.232767,0.00001514059,0.00005458631,0.000001590422,0.0002430094,0.00001861028,0.7558601,0.0005819634,0.003400689,0.005834355],"study_design_scores_gemma":[0.0009108867,0.0004780369,0.9779069,0.000005487489,0.00006396859,0.000001396393,0.0001754295,0.01307046,0.005316212,0.001581415,0.0003280353,0.0001618008],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9932639,0.0002957644,0.005640568,0.0003731797,0.00008164616,0.0001938007,0.00007601883,0.000009698834,0.00006544506],"genre_scores_gemma":[0.9954619,0.0001086981,0.003325962,0.0001450155,0.00007088297,0.0000734874,0.000213777,0.000009425998,0.0005908135],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7505439,"threshold_uncertainty_score":0.3795694,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04188081530055364,"score_gpt":0.327757849187102,"score_spread":0.2858770338865484,"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."}}