{"id":"W2048065755","doi":"10.1142/s0219720009004242","title":"A TUTORIAL OF TECHNIQUES FOR IMPROVING STANDARD HIDDEN MARKOV MODEL ALGORITHMS","year":2009,"lang":"en","type":"article","venue":"Journal of Bioinformatics and Computational Biology","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Viterbi algorithm; Computer science; Hidden Markov model; Logarithm; Forward algorithm; Heuristics; Algorithm; Factor (programming language); Heuristic; Markov model; Sequence (biology); Markov chain; Space (punctuation); Parallel computing; Artificial intelligence; Mathematics; Variable-order Markov model; Machine learning; Programming language","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":[],"consensus_categories":[],"category_scores_codex":[0.0004484473,0.00009092274,0.0002505663,0.0001530012,0.00006899266,0.00005321088,0.0003119219,0.0000723488,7.526467e-7],"category_scores_gemma":[0.00004326434,0.00006637294,0.00007360879,0.00007847211,0.00004011444,0.0004458106,0.00008825625,0.00008715643,1.145149e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001975858,"about_ca_system_score_gemma":0.0001726603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001293725,"about_ca_topic_score_gemma":7.422689e-8,"domain_scores_codex":[0.9990099,0.00001499444,0.0006100975,0.00007572453,0.0001676371,0.0001216125],"domain_scores_gemma":[0.9987065,0.0001241269,0.0005903861,0.00009208439,0.0004263678,0.00006053076],"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":[0.00007508008,0.00003048457,0.00001039972,0.00002657711,0.0000164896,7.345265e-7,0.0002192963,0.0005900439,0.0003948433,0.02723098,0.0005484744,0.9708566],"study_design_scores_gemma":[0.0005368925,0.001116614,0.00007056718,0.00002408193,0.000008089396,0.00005401771,0.00001748524,0.9022201,0.0004252632,0.09496266,0.0004862898,0.00007799616],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002368333,0.0001205913,0.9967175,0.0003476253,0.0001961702,0.0001030568,0.00005474806,0.00001236266,0.00007963022],"genre_scores_gemma":[0.08047592,0.00002673968,0.9191872,0.0001605701,0.0001318562,0.000001051361,0.00001140823,0.000001994482,0.000003281601],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9707786,"threshold_uncertainty_score":0.270661,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01131390067321352,"score_gpt":0.2736569663899769,"score_spread":0.2623430657167634,"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."}}