{"id":"W2166372712","doi":"10.1093/bioinformatics/btu544","title":"Figmop: a profile HMM to identify genes and bypass troublesome gene models in draft genomes","year":2014,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Gene; Genome; Computational biology; Hidden Markov model; Biology; Genetics; Computer science; Artificial intelligence","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":[0.0002037086,0.0001824762,0.0002056539,0.00008695588,0.00007494757,0.00005352018,0.0001787778,0.0001085862,0.000004715268],"category_scores_gemma":[0.00002603829,0.0001695585,0.00004683008,0.00009043492,0.00004482405,0.000004100488,0.0002714207,0.00004763079,0.00002063765],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001271801,"about_ca_system_score_gemma":0.00003312913,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001649265,"about_ca_topic_score_gemma":0.00004824284,"domain_scores_codex":[0.9990281,0.00002094596,0.0003404725,0.0002022148,0.0001081221,0.0003000726],"domain_scores_gemma":[0.9994471,0.00001242294,0.00007604545,0.0003008886,0.00005516391,0.0001083841],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001548504,0.0001454915,0.01566301,0.0004409031,0.0002471956,0.000003199639,0.004249702,0.006073943,0.8843099,0.001470633,0.004664266,0.08257687],"study_design_scores_gemma":[0.00746984,0.002595844,0.08894344,0.0001675195,0.0001785171,0.0001256858,0.002836494,0.1660118,0.4055336,0.005751874,0.3166074,0.00377804],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9902991,0.002345947,0.004694732,0.000129854,0.0001105873,0.0003522804,0.00005726648,0.00000656207,0.002003648],"genre_scores_gemma":[0.9708518,0.001142412,0.02695105,0.0004876056,0.0001410147,0.00005982205,0.00005646257,0.00002142746,0.0002884387],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4787763,"threshold_uncertainty_score":0.6914393,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0148272516669068,"score_gpt":0.2479729135171589,"score_spread":0.2331456618502521,"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."}}