{"id":"W2138379687","doi":"10.1093/bioinformatics/18.8.1034","title":"Improving gene recognition accuracy by combiningpredictions from two gene-finding programs","year":2002,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genomics and Chromatin Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Children's & Women's Health Centre of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Exon; Correctness; Computer science; Gene prediction; Gene; Consistency (knowledge bases); Computational biology; Genetics; Data mining; Artificial intelligence; Biology; Algorithm; Genome","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00009695582,0.0001875378,0.0001359101,0.0000392087,0.0001947631,0.0001234151,0.0002090939,0.000152571,0.00004267013],"category_scores_gemma":[0.00006576238,0.0001958541,0.00009056902,0.00008828236,0.00004801075,0.00001874416,0.0001221042,0.000112133,0.00009070842],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003189719,"about_ca_system_score_gemma":0.0000220674,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005068339,"about_ca_topic_score_gemma":0.00001321918,"domain_scores_codex":[0.998946,0.00001677355,0.0004156638,0.0001884032,0.000138011,0.0002951392],"domain_scores_gemma":[0.9992083,0.00001560152,0.0002554691,0.0003400483,0.00006983396,0.0001107879],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002086067,0.0002396801,0.002713543,0.00006537543,0.0002182554,0.000002864845,0.000978899,0.0001849592,0.6022373,0.00002026023,0.008742957,0.3845751],"study_design_scores_gemma":[0.003804082,0.000721748,0.0003934194,0.00007363209,0.0001864177,0.00009073711,0.001121014,0.7455482,0.2213666,0.0003308488,0.02504457,0.00131871],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9756164,0.0004084574,0.02171701,0.00004132888,0.0002966177,0.0002639428,0.0004041168,0.00005762812,0.001194532],"genre_scores_gemma":[0.9218596,0.0006015057,0.07079334,0.0002577403,0.0003187754,0.00003801478,0.005860368,0.00004281395,0.000227813],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7453632,"threshold_uncertainty_score":0.7986699,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01937588661777488,"score_gpt":0.22736097331829,"score_spread":0.2079850867005151,"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."}}