{"id":"W2345208044","doi":"10.1016/j.trb.2016.04.007","title":"Hidden Markov Model-based population synthesis","year":2016,"lang":"en","type":"article","venue":"Transportation Research Part B Methodological","topic":"demographic modeling and climate adaptation","field":"Decision Sciences","cited_by":87,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Hidden Markov model; Population; Markov chain Monte Carlo; Computer science; Markov chain; Marginal distribution; Boundary (topology); Statistics; Econometrics; Algorithm; Machine learning; Mathematics; Artificial intelligence; Bayesian probability; Random variable","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":["metaresearch","insufficient_payload"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.03176739,0.0001775331,0.0004063412,0.0006463952,0.000348654,0.0001220974,0.0006299549,0.0002431081,0.001925588],"category_scores_gemma":[0.01523831,0.0000977751,0.0002397,0.001222511,0.0002864011,0.000342209,0.00001225985,0.000255737,0.0002773551],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005047253,"about_ca_system_score_gemma":0.00009237367,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008308455,"about_ca_topic_score_gemma":0.000172698,"domain_scores_codex":[0.9897949,0.004492299,0.001028444,0.0008741207,0.003231728,0.0005785124],"domain_scores_gemma":[0.9755331,0.02244421,0.0001933904,0.0006293608,0.0009394326,0.0002605555],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000898071,0.0002153426,0.1028901,0.00001897554,0.00002745342,0.00002414225,0.000204419,0.007327357,0.00663302,0.02233767,0.004702191,0.8547212],"study_design_scores_gemma":[0.0008342037,0.0002023857,0.5755679,0.00008646168,0.00003598368,9.129814e-7,0.0003350783,0.1014486,0.002360935,0.3167598,0.001958547,0.0004092417],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.441487,0.00002218358,0.5535489,0.003800196,0.00009579966,0.0002306652,0.00005620693,0.0001022503,0.0006568492],"genre_scores_gemma":[0.894553,0.0000509742,0.1042569,0.0001145192,0.00006312842,0.0001763707,0.00002883854,0.00001580213,0.0007404382],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.854312,"threshold_uncertainty_score":0.9989868,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7043500989976119,"score_gpt":0.5514639610379705,"score_spread":0.1528861379596415,"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."}}