{"id":"W2094353923","doi":"10.1890/11-2241.1","title":"Flexible and practical modeling of animal telemetry data: hidden Markov models and extensions","year":2012,"lang":"en","type":"article","venue":"Ecology","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":478,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Center for Northern Studies","funders":"Engineering and Physical Sciences Research Council","keywords":"Hidden Markov model; Computer science; Markov model; Markov chain; Variable-order Bayesian network; Bayesian probability; Machine learning; Variable-order Markov model; Artificial intelligence; Bayesian inference","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":[0.004736023,0.0008817431,0.001027254,0.0009292185,0.0006062519,0.001265467,0.002395892,0.002037133,0.0028636],"category_scores_gemma":[0.02013018,0.0009489377,0.001420517,0.001493216,0.001131485,0.002895752,0.001686585,0.002377085,0.0006578417],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001022725,"about_ca_system_score_gemma":0.001335591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01008226,"about_ca_topic_score_gemma":0.009411166,"domain_scores_codex":[0.9985871,0.0007080501,0.00009207278,0.0002566327,0.0002566405,0.00009957445],"domain_scores_gemma":[0.9899362,0.008168682,0.0007212767,0.0006897919,0.0003356645,0.0001483756],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002468993,0.00004223162,0.00171481,0.0001047709,0.00003412186,0.0001377931,0.0001732248,0.8753603,0.0004491027,0.09644025,0.0008134139,0.02470543],"study_design_scores_gemma":[0.000004234483,0.000008190679,0.000204733,0.00001772412,0.000005387791,0.00003041318,0.00001305614,0.9434283,0.00006926528,0.05545005,0.000757879,0.00001075987],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008429693,0.0003485552,0.9894427,0.0005704134,0.00003657995,0.00003337024,0.0002241724,0.0001448597,0.0007696576],"genre_scores_gemma":[0.4895671,0.0028148,0.4998395,0.0003878514,0.0003114838,0.0005576194,0.001255922,0.0001957299,0.005070092],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01008226,"threshold_uncertainty_score":0.02504677,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07015033126805864,"score_gpt":0.3071856308862854,"score_spread":0.2370352996182267,"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."}}