{"id":"W2793784779","doi":"10.1093/bioinformatics/bty046","title":"pymzML v2.0: introducing a highly compressed and seekable gzip format","year":2018,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":90,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Jewish General Hospital","funders":"","keywords":"Computer science; Random access; Scripting language; File format; Documentation; Code (set theory); Source code; Scheme (mathematics); Field (mathematics); MIT License; Data file; Data compression; Algorithm; Software; Computational science; Computer engineering; Database; Programming language; Set (abstract data type)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002067674,0.0001649103,0.0001965769,0.00006755866,0.0001985207,0.00006618213,0.0001472567,0.00009128446,0.00001948403],"category_scores_gemma":[0.0001253273,0.0001396139,0.00004474195,0.0001052068,0.0001536991,0.00001498612,0.0002670467,0.00006517971,0.0000388772],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009337475,"about_ca_system_score_gemma":0.00002926933,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001152537,"about_ca_topic_score_gemma":0.00001707931,"domain_scores_codex":[0.9991159,0.00001322781,0.0002967662,0.0001609694,0.0001140209,0.0002990832],"domain_scores_gemma":[0.9993291,0.00001434331,0.0001237643,0.0003431886,0.0001060924,0.00008349996],"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":[0.0003857361,0.000208083,0.007319304,0.0006983367,0.0007861094,0.000003461461,0.003515552,0.00004013583,0.7149982,0.01020222,0.2334685,0.02837437],"study_design_scores_gemma":[0.001377695,0.0008074517,0.002367716,0.00002921159,0.00006221847,0.00004512309,0.0007688974,0.01049892,0.2187677,0.0003391676,0.7643825,0.0005534719],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.955278,0.001533863,0.01768362,0.0004309195,0.0006132162,0.0003594273,0.00005072518,0.00005619354,0.02399407],"genre_scores_gemma":[0.9642839,0.001018745,0.03260967,0.0007125795,0.0006067402,0.00001217468,0.00006766796,0.00001962865,0.0006688794],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5309139,"threshold_uncertainty_score":0.5693288,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008613553688783167,"score_gpt":0.2295192498196013,"score_spread":0.2209056961308182,"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."}}