{"id":"W1956232549","doi":"10.1007/3-540-45452-7_17","title":"A Better Method for Length Distribution Modeling in HMMs and Its Application to Gene Finding","year":2002,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Fractal and DNA sequence analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Hidden Markov model; Minimum description length; Sequence (biology); Generalization; Algorithm; Computer science; Geometric distribution; Distribution (mathematics); Abstraction; Pattern recognition (psychology); Probability distribution; Artificial intelligence; Mathematics; Statistics; Biology; Mathematical analysis","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005816526,0.001966641,0.002794458,0.002063157,0.001654775,0.002896336,0.003750158,0.004678901,0.01133581],"category_scores_gemma":[0.01986563,0.002230582,0.003213187,0.003118727,0.001571286,0.006959942,0.002428604,0.007190206,0.007016382],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001376062,"about_ca_system_score_gemma":0.001307358,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003856743,"about_ca_topic_score_gemma":0.003812371,"domain_scores_codex":[0.9968222,0.00116281,0.0002947484,0.0008681387,0.0007352993,0.000116752],"domain_scores_gemma":[0.9887218,0.006306862,0.000295932,0.003118197,0.0013252,0.0002319616],"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":[0.0004693414,0.0003644948,0.001770961,0.0004704671,0.0004030098,0.0003768089,0.0007091177,0.1664559,0.02653196,0.1565461,0.018225,0.6276767],"study_design_scores_gemma":[0.00005990954,0.00004934985,0.000290674,0.00004212928,0.00007307248,0.0003076455,0.00003356566,0.8926191,0.008236974,0.08372654,0.01444746,0.0001135724],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0004370903,0.00006333504,0.9982035,0.00006166349,0.00005084204,0.00001005062,0.000048071,0.001035589,0.00008982171],"genre_scores_gemma":[0.01195512,0.0001871382,0.9830663,0.0001468261,0.0001379322,0.000153878,0.0004192002,0.00152522,0.002408347],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01133581,"threshold_uncertainty_score":0.03792208,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02209476496238248,"score_gpt":0.2766904096219368,"score_spread":0.2545956446595543,"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."}}