{"id":"W1984599252","doi":"10.1186/1471-2105-12-299","title":"Ranking insertion, deletion and nonsense mutations based on their effect on genetic information","year":2011,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Evolution and Genetic Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Canadian Institutes of Health Research","keywords":"Biology; Genetics; Nonsense mutation; Indel; INDEL Mutation; Coding region; Mutation; Context (archaeology); Computational biology; Single-nucleotide polymorphism; Gene; Missense mutation; Genotype","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":[0.0002058044,0.00017192,0.000104891,0.0001321281,0.0001396543,0.00003524372,0.00008525751,0.0001429273,0.00001212717],"category_scores_gemma":[0.0001308794,0.0001454167,0.00006082558,0.000100967,0.00006933553,0.00001554876,0.00002829047,0.00007982685,0.00006165886],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001831729,"about_ca_system_score_gemma":0.00005963776,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005672559,"about_ca_topic_score_gemma":0.00002178036,"domain_scores_codex":[0.9992,0.00006117237,0.0003206366,0.0001077616,0.000138296,0.0001721235],"domain_scores_gemma":[0.9993786,0.00003533044,0.0001399115,0.0002866703,0.00008339184,0.00007609826],"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":[0.008904302,0.00158541,0.1642869,0.003209355,0.0005152855,0.000008287649,0.01544978,0.3429092,0.03235568,0.005588006,0.009881934,0.4153059],"study_design_scores_gemma":[0.001907343,0.001972793,0.1072738,0.00005906859,0.00003425534,0.00002253739,0.0002370135,0.877776,0.008064025,0.0001287249,0.002163719,0.0003607353],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.671654,0.00002351728,0.3233251,0.00002801083,0.0001265779,0.0003915075,0.00003286216,0.00003839825,0.004380113],"genre_scores_gemma":[0.9625772,0.0000263121,0.03586053,0.001036112,0.0000348668,0.00002870723,0.0003894222,0.00001281737,0.00003408547],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5348668,"threshold_uncertainty_score":0.5929921,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01021817641883853,"score_gpt":0.2079564167870597,"score_spread":0.1977382403682212,"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."}}