{"id":"W2401728283","doi":"","title":"State sequence analysis in hidden Markov models","year":2015,"lang":"en","type":"article","venue":"Uncertainty in Artificial Intelligence","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ottawa Hospital","funders":"","keywords":"Hidden Markov model; Viterbi algorithm; Sequence (biology); Inference; Forward algorithm; Computer science; State (computer science); Markov chain; Markov model; Hidden semi-Markov model; Sequence labeling; Artificial intelligence; Algorithm; Machine learning; Markov property; Variable-order Markov model","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002031681,0.0002769792,0.0005361511,0.001157994,0.00005450543,0.0003042618,0.001346501,0.0001158904,0.00003636131],"category_scores_gemma":[0.0003630797,0.000289524,0.0001481097,0.004688156,0.0001281011,0.001325582,0.0003146759,0.0003679778,0.0002534621],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005958209,"about_ca_system_score_gemma":0.0003573396,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.009547822,"about_ca_topic_score_gemma":0.02854497,"domain_scores_codex":[0.9963802,0.0004606009,0.0009976295,0.0008710405,0.0006617194,0.0006288056],"domain_scores_gemma":[0.9978887,0.0004350147,0.0002180255,0.0008611952,0.0003451391,0.0002519437],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008123909,0.0002598165,0.003417168,0.00001239885,0.00007090239,0.0001992183,0.009891306,0.3656202,0.0002815322,0.01693789,0.00006672068,0.6031616],"study_design_scores_gemma":[0.00005742847,0.00004694526,0.0001672291,0.00004060019,0.00001060059,0.000007292019,0.0008055419,0.8543711,0.001247954,0.1428512,0.00007639819,0.0003177252],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1681252,0.00009904057,0.8276244,0.0008934814,0.000444124,0.0004376821,0.00001840994,0.0001634038,0.002194311],"genre_scores_gemma":[0.994396,0.0000182505,0.005108057,0.0002086565,0.00004090423,0.00007252171,0.000009379162,0.00001190897,0.000134314],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8262709,"threshold_uncertainty_score":0.9999557,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1614388410284468,"score_gpt":0.3358911258933971,"score_spread":0.1744522848649503,"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."}}