{"id":"W4387359355","doi":"10.1093/bioinformatics/btag207","title":"Segzoo: a turnkey system that summarizes genome annotations","year":2023,"lang":"en","type":"preprint","venue":"Bioinformatics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; Princess Margaret Cancer Centre; University of Toronto; University Health Network","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Python (programming language); Annotation; Turnkey; Upload; Visualization; MIT License; JSON; Genome; Source code; Unix; Download; Software; Data mining; Operating system; Database; Artificial intelligence; Biology","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002288861,0.0003754944,0.0003636818,0.0001134619,0.0001755091,0.0001272128,0.0004699853,0.0004189101,0.00000377759],"category_scores_gemma":[0.0000441171,0.0003570119,0.0002371965,0.00007200986,0.00008477628,0.000001162285,0.001360986,0.0002148695,0.0001696891],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005015441,"about_ca_system_score_gemma":0.0001947271,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004277924,"about_ca_topic_score_gemma":0.00005076521,"domain_scores_codex":[0.9985366,0.00003124334,0.0005187795,0.0003238212,0.0002172754,0.0003723159],"domain_scores_gemma":[0.9985701,0.00002272948,0.0003414106,0.000801546,0.0001552852,0.0001089165],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008677861,0.001038155,0.06483722,0.04955861,0.02316643,0.0001942703,0.04273751,0.1220711,0.379136,0.009871975,0.2774162,0.02910469],"study_design_scores_gemma":[0.004636272,0.001379272,0.1423572,0.00147292,0.001769969,0.0002179855,0.02871639,0.05121574,0.02860168,0.002250531,0.728461,0.008921096],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.944802,0.006503194,0.01653278,0.0006237712,0.005832498,0.002625522,0.005160009,0.0002520319,0.01766817],"genre_scores_gemma":[0.9719061,0.002459174,0.01923171,0.0002512072,0.000780795,0.0002362836,0.002693323,0.0001109372,0.002330445],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4510447,"threshold_uncertainty_score":0.9998882,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03593523873103464,"score_gpt":0.2498236841964263,"score_spread":0.2138884454653917,"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."}}