{"id":"W3214736048","doi":"10.31390/josa.2.4.03","title":"Recursive and Viterbi Estimation for Semi-Markov Chains","year":2021,"lang":"en","type":"article","venue":"Journal of Stochastic Analysis","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Australian Research Council; Natural Sciences and Engineering Research Council of Canada","keywords":"Viterbi algorithm; Markov chain; Computer science; Estimation; Iterative Viterbi decoding; Markov model; Soft output Viterbi algorithm; Algorithm; Artificial intelligence; Hidden Markov model; Machine learning; Economics; Decoding methods; Sequential decoding","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001752769,0.00007607734,0.000299326,0.0002344253,0.00003999916,0.00003960152,0.00003755179,0.00004446974,0.0000193835],"category_scores_gemma":[0.0001472194,0.00006935174,0.0002011781,0.0003693459,0.000009900612,0.0000845351,0.00000470024,0.00008254577,0.000001070783],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004960396,"about_ca_system_score_gemma":0.00001620025,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001943519,"about_ca_topic_score_gemma":0.00001045752,"domain_scores_codex":[0.9993849,0.00001716491,0.0003092213,0.0000673353,0.0001283628,0.00009297135],"domain_scores_gemma":[0.999429,0.0001193748,0.000118936,0.00007061884,0.0001881313,0.00007392735],"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":[0.00004460934,0.00001898568,0.00004186832,0.00006376471,0.002959272,0.00001641546,0.000439233,0.9740436,0.003438657,0.0003036326,0.0003046711,0.01832529],"study_design_scores_gemma":[0.0004588491,0.00004720977,0.0002425473,0.00003611329,0.001204861,0.00007447381,0.0003933765,0.9968695,0.0001999653,0.0002705944,0.0001258395,0.00007669246],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04084658,0.0008764023,0.9577993,0.0001192428,0.0002118981,0.00004685918,0.000007778563,0.00001387006,0.00007809758],"genre_scores_gemma":[0.997521,0.0000177751,0.00215939,0.00002596566,0.0001244193,0.000004782929,0.000003391468,0.000008746832,0.0001344694],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9566745,"threshold_uncertainty_score":0.2828082,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005134301881481835,"score_gpt":0.2234043029175558,"score_spread":0.218270001036074,"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."}}