{"id":"W6931664732","doi":"10.5281/zenodo.5784787","title":"Assessing and assuring interoperability of a genomics file format: results and scripts","year":2022,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre","funders":"","keywords":"Interoperability; Scripting language; File format; Test (biology); Genomics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.01241087,0.002665926,0.001234181,0.004887455,0.001617914,0.00447148,0.003347139,0.001318404,0.07947646],"category_scores_gemma":[0.05030213,0.001125234,0.001452332,0.003622591,0.0009737785,0.00338124,0.003674771,0.001701909,0.06854209],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001769564,"about_ca_system_score_gemma":0.003643068,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009215959,"about_ca_topic_score_gemma":0.005923554,"domain_scores_codex":[0.9889341,0.002527694,0.001531444,0.001502751,0.00489288,0.0006111648],"domain_scores_gemma":[0.9653625,0.01195141,0.001310848,0.007798512,0.01263367,0.0009430356],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001507926,0.0006012578,0.005421667,0.001141615,0.0001740009,0.0003784441,0.0005589767,0.006788366,0.006988869,0.005384321,0.880676,0.09037869],"study_design_scores_gemma":[0.001286903,0.0005542464,0.02263779,0.0008734646,0.0003102625,0.001031719,0.001204698,0.07508468,0.145524,0.01944076,0.7313023,0.0007490723],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.01720469,0.0002299632,0.1307932,0.001264082,0.0004962336,0.002267197,0.4793518,0.3395225,0.02887025],"genre_scores_gemma":[0.03888626,0.0002142853,0.1769599,0.0004772088,0.00009796178,0.003522297,0.6632448,0.1023824,0.01421477],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.07947646,"threshold_uncertainty_score":0.2658752,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03452343956077849,"score_gpt":0.2602770093946838,"score_spread":0.2257535698339053,"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."}}