{"id":"W4205892261","doi":"10.1007/s13253-021-00483-x","title":"Hidden Markov and Semi-Markov Models When and Why are These Models Useful for Classifying States in Time Series Data?","year":2022,"lang":"en","type":"article","venue":"Journal of Agricultural Biological and Environmental Statistics","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Agencia Nacional de Promoción Científica y Tecnológica","keywords":"Hidden Markov model; Autoregressive model; Computer science; Artificial intelligence; Context (archaeology); Hidden semi-Markov model; Machine learning; Markov model; Markov chain; Time series; Pattern recognition (psychology); Data mining; Variable-order Markov model; Econometrics; Mathematics; Geography","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":[0.007588756,0.0006523513,0.001533305,0.001446975,0.0006490802,0.002281811,0.001480705,0.002406236,0.002978425],"category_scores_gemma":[0.04432666,0.0006568713,0.001555912,0.001476058,0.001861424,0.008556862,0.0009635212,0.002684986,0.0007624236],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001348381,"about_ca_system_score_gemma":0.001401341,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007949997,"about_ca_topic_score_gemma":0.008400163,"domain_scores_codex":[0.9974215,0.001206829,0.0001941991,0.0005131796,0.0004264972,0.0002378212],"domain_scores_gemma":[0.9495683,0.04388008,0.001826249,0.002317319,0.001885412,0.0005227147],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0009011612,0.0002814906,0.04833469,0.0007685748,0.0008450018,0.0003044899,0.001001761,0.249071,0.001831146,0.4356479,0.013457,0.2475558],"study_design_scores_gemma":[0.00003317912,0.00006146051,0.004677375,0.0001015575,0.00007628705,0.00009875027,0.000151215,0.6448383,0.0005141218,0.3479705,0.00142805,0.00004927445],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1376619,0.004496387,0.840883,0.009371615,0.0007190055,0.00008984428,0.001567349,0.0007376599,0.004473174],"genre_scores_gemma":[0.9313563,0.002209076,0.06034845,0.0009561875,0.0007917742,0.0001701781,0.001419405,0.00009743611,0.002651357],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007949997,"threshold_uncertainty_score":0.04013366,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03681079504648487,"score_gpt":0.2241440705376619,"score_spread":0.187333275491177,"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."}}