{"id":"W2147781067","doi":"10.1002/cjs.11225","title":"Markov chain order estimation based on the chi‐square divergence","year":2014,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"CHIST-ERA; Fundação de Apoio à Pesquisa do Distrito Federal; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Agência Nacional de Águas","keywords":"Markov chain; Mathematics; Estimation; Divergence (linguistics); Square (algebra); Order (exchange); Statistics; Computer science; Applied mathematics; Economics; Philosophy; Geometry","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002414086,0.00007142485,0.00009302676,0.00009823152,0.00009464618,0.00004490145,0.0001046992,0.00003045319,0.0001959588],"category_scores_gemma":[0.0003526129,0.00005403937,0.00002191832,0.0001073516,0.00002149629,0.00002820027,9.342347e-7,0.0001444178,0.000026158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008741383,"about_ca_system_score_gemma":0.0001131083,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004405317,"about_ca_topic_score_gemma":0.005548402,"domain_scores_codex":[0.999469,0.00004675758,0.0001874579,0.00003731435,0.0001261329,0.0001333124],"domain_scores_gemma":[0.9994292,0.0001206405,0.00005888472,0.0000943321,0.0001012411,0.0001956485],"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":[0.00001302623,0.000005542984,0.001139863,0.00006901949,0.00004574125,0.00004613911,0.0003367526,0.8465939,0.00005139102,0.01133736,0.04827614,0.09208508],"study_design_scores_gemma":[0.0001720176,0.00006035904,0.002151519,0.00004639395,0.000009294135,0.00001044706,0.00004639384,0.9780368,0.00001774571,0.0002499734,0.01913149,0.00006751398],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004800565,0.00004403909,0.9905324,0.0004590472,0.001258283,0.0000819267,0.0001067155,0.00001401633,0.002703009],"genre_scores_gemma":[0.9965342,0.000002258541,0.003040937,0.0002350906,0.000105285,0.000001962597,0.000002743841,0.00001241397,0.00006515694],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9917336,"threshold_uncertainty_score":0.3096139,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006397887291464874,"score_gpt":0.1815936269644522,"score_spread":0.1751957396729873,"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."}}