{"id":"W4280553929","doi":"10.1093/bioinformatics/btac327","title":"Assessing and assuring interoperability of a genomics file format","year":2022,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; University of Toronto; Princess Margaret Cancer Centre; University Health Network","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Software; Suite; Interoperability; Test suite; File format; Fuzz testing; Software engineering; Correctness; Test case; Database; Programming language; Operating system; Machine learning","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04170804,0.001492505,0.0007718175,0.004070838,0.001630319,0.00417034,0.002985488,0.001791289,0.003179643],"category_scores_gemma":[0.1789893,0.0009455204,0.00126712,0.00235819,0.002461728,0.006027195,0.004874381,0.001850391,0.002043946],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001648155,"about_ca_system_score_gemma":0.003016674,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00370117,"about_ca_topic_score_gemma":0.002383617,"domain_scores_codex":[0.970612,0.006694913,0.004820011,0.004689508,0.01194593,0.001237571],"domain_scores_gemma":[0.8130282,0.0983341,0.01900861,0.03031795,0.03649567,0.002815364],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.004112156,0.001292535,0.4413964,0.002969343,0.0006558807,0.001389151,0.005897249,0.03953033,0.190264,0.01556412,0.02188825,0.2750405],"study_design_scores_gemma":[0.000219961,0.001721074,0.1276918,0.0007825449,0.0004837967,0.001534157,0.001134407,0.1596196,0.6586074,0.01699138,0.03071672,0.0004970801],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5729061,0.0008175409,0.3624023,0.001165378,0.0001980755,0.001210523,0.008121166,0.04717516,0.006003806],"genre_scores_gemma":[0.6984926,0.0002446608,0.2799014,0.0004467187,0.00006203261,0.0006550416,0.01332436,0.006002433,0.0008707938],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.04170804,"threshold_uncertainty_score":0.2205758,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0172935599137878,"score_gpt":0.2410513740747336,"score_spread":0.2237578141609458,"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."}}