{"id":"W1798235558","doi":"","title":"Evidence combination in hidden markov models for gene prediction","year":2006,"lang":"en","type":"dissertation","venue":"UWSpace (University of Waterloo)","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Genome; Hidden Markov model; Gene prediction; Probabilistic logic; Annotation; Computational biology; Computer science; Genome project; Sequence (biology); Gene; Markov chain; Gene Annotation; Identification (biology); Genetics; Biology; Artificial intelligence; Machine learning","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":[0.01092242,0.001658583,0.003538589,0.002999705,0.001086609,0.002960338,0.003521559,0.002897264,0.004271375],"category_scores_gemma":[0.03323796,0.002122651,0.002480092,0.002966681,0.00199655,0.004393669,0.002745585,0.005080659,0.001238781],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002719777,"about_ca_system_score_gemma":0.00230644,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007575603,"about_ca_topic_score_gemma":0.007815257,"domain_scores_codex":[0.9942041,0.003900548,0.0003205497,0.0007123696,0.0005843734,0.0002780157],"domain_scores_gemma":[0.942864,0.05317209,0.001285544,0.001090647,0.001165706,0.0004220902],"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":[0.0003576073,0.0001042263,0.001638905,0.0002493382,0.0003455214,0.0002047308,0.0001565964,0.8655972,0.0002841363,0.04304083,0.002322288,0.08569859],"study_design_scores_gemma":[0.00001652402,0.00001997166,0.0001103482,0.00001984769,0.00002440218,0.0000128031,0.000006116466,0.9634019,0.0001061818,0.03601066,0.0002575891,0.00001372902],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0118447,0.001869604,0.9820731,0.001212843,0.0001256193,0.0001024598,0.000398811,0.0009275898,0.00144522],"genre_scores_gemma":[0.5075932,0.002581893,0.4750711,0.0008544771,0.001037018,0.001125667,0.002766263,0.0004226029,0.008547844],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01092242,"threshold_uncertainty_score":0.05776393,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01420226556176271,"score_gpt":0.2062370289198014,"score_spread":0.1920347633580387,"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."}}