{"id":"W4206122360","doi":"10.1101/2022.01.07.475366","title":"Assessing and assuring interoperability of a genomics file format","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"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; University Health Network","keywords":"Computer science; File format; Interoperability; Software; Documentation; Software engineering; Test suite; 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":[0.0279348,0.001215053,0.0007342856,0.005232913,0.001020604,0.003602946,0.002528339,0.001566386,0.001802901],"category_scores_gemma":[0.1460071,0.0007862884,0.00129314,0.002037669,0.00195276,0.004699186,0.003597405,0.001566514,0.0007280064],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001690736,"about_ca_system_score_gemma":0.002225815,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003031005,"about_ca_topic_score_gemma":0.00195187,"domain_scores_codex":[0.9688344,0.006871964,0.004940352,0.004192293,0.01383505,0.001325986],"domain_scores_gemma":[0.7899429,0.1169209,0.02192801,0.03272483,0.03650952,0.001973924],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002810069,0.001321241,0.4446006,0.001617017,0.0006563032,0.00149796,0.004489131,0.06197622,0.1615971,0.01340267,0.007679137,0.2983526],"study_design_scores_gemma":[0.000156594,0.001799025,0.1019958,0.0005467544,0.0004326475,0.001299066,0.001117934,0.2959423,0.5729116,0.009662922,0.01374866,0.0003867343],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6870493,0.0003992951,0.2807896,0.0005356268,0.00008805747,0.0005618045,0.002312464,0.02542837,0.002835413],"genre_scores_gemma":[0.834327,0.00009345942,0.1586973,0.0002107871,0.00002634244,0.0003331631,0.003507502,0.00235733,0.0004470308],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0279348,"threshold_uncertainty_score":0.1477351,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01798647892992147,"score_gpt":0.2481246178455489,"score_spread":0.2301381389156275,"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."}}