{"id":"W4385212284","doi":"10.1007/978-3-031-33429-0_3","title":"Discrete-Time Semi-Markov Random Evolutions","year":2023,"lang":"en","type":"book-chapter","venue":"Probability and its applications","topic":"Technology and Human Factors in Education and Health","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Markov chain; Statistical physics; Mathematics; Discrete time and continuous time; Markov process; Variable-order Markov model; Markov renewal process; Applied mathematics; Markov property; Scaling; Series (stratigraphy); Markov model; Computer science; Physics; Statistics; Geometry","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0002637854,0.0002475871,0.000468897,0.000174959,0.0004771148,0.00001698073,0.0001238082,0.0005424382,0.001274534],"category_scores_gemma":[0.0001023676,0.0002270088,0.0001373596,0.00007786814,0.0003479118,0.00004352766,0.00007787465,0.0006700328,0.001166129],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001163865,"about_ca_system_score_gemma":0.0002901472,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006098768,"about_ca_topic_score_gemma":0.00002611839,"domain_scores_codex":[0.9986338,0.00001455475,0.000427165,0.0005438044,0.0001554826,0.0002251924],"domain_scores_gemma":[0.9986579,0.0001993403,0.000149504,0.0006021808,0.0001834606,0.0002076529],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000510357,0.0001327186,0.00008844669,0.0005084751,0.00009478689,5.073626e-7,0.0002010479,3.756919e-7,0.00002823678,0.9841744,0.00845391,0.006266026],"study_design_scores_gemma":[0.0006506017,0.00008279371,0.001815601,0.0001561965,0.0002804224,0.00001818078,0.0000254057,0.00006239173,0.000007909134,0.358977,0.6376581,0.0002655186],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.00128574,0.00487814,0.001219968,0.02859736,0.0002924702,0.008876329,0.0009831203,0.001960528,0.9519063],"genre_scores_gemma":[0.01476287,0.003095929,0.0007197937,0.000468604,0.0003526989,0.001076965,0.0008515037,0.0000672223,0.9786044],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.6292041,"threshold_uncertainty_score":0.9996384,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03217627475832285,"score_gpt":0.2996980516115382,"score_spread":0.2675217768532154,"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."}}