{"id":"W2898760629","doi":"10.1101/459552","title":"GenPipes: an open-source framework for distributed and scalable genomic analyses","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke; McGill University; National Research Council Canada; Compute Canada; McGill University and Génome Québec Innovation Centre; Université de Montréal; Ontario Genomics","funders":"","keywords":"Computer science; Scalability; Workflow; Cloud computing; MIT License; Python (programming language); Genomics; Software; Metagenomics; Software deployment; Server; Distributed computing; Data science; Software engineering; Database; World Wide Web; Genome; Operating system; Biology","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004440534,0.0005594722,0.0005910581,0.00008132528,0.0003491527,0.0004115467,0.0009759885,0.0006954904,0.00001082135],"category_scores_gemma":[0.0002424119,0.0005879339,0.0001253731,0.0001382387,0.0002411441,0.000004056682,0.001997264,0.0002451666,0.000006295452],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005522924,"about_ca_system_score_gemma":0.000326796,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006562829,"about_ca_topic_score_gemma":0.000008573587,"domain_scores_codex":[0.9973949,0.00009633446,0.0004328822,0.001390402,0.0001287833,0.0005567676],"domain_scores_gemma":[0.9974247,0.00003945328,0.0003467886,0.00144009,0.0004612381,0.0002877839],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001012753,0.0001014341,0.006910167,0.0001333415,0.0004595918,0.000001631374,0.000008095484,0.0001283978,0.9913009,0.0001792133,0.0006705055,0.000005436088],"study_design_scores_gemma":[0.001116333,0.0008294502,0.1229239,0.0001839162,0.0004896549,6.090009e-8,0.0000185674,0.0006398796,0.802607,0.0001395695,0.06930577,0.001745884],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9566551,0.004379568,0.03586724,0.00008090997,0.0004826219,0.001031455,0.001467145,0.00003135006,0.000004582685],"genre_scores_gemma":[0.9504598,0.001003726,0.04669319,0.0002729355,0.001079211,0.0003256189,0.000009722316,0.0001469294,0.000008883163],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1886939,"threshold_uncertainty_score":0.9996572,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0306196607450594,"score_gpt":0.2841129563815331,"score_spread":0.2534932956364737,"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."}}