{"id":"W2949355358","doi":"10.1093/gigascience/giz037","title":"GenPipes: an open-source framework for distributed and scalable genomic analyses","year":2019,"lang":"en","type":"article","venue":"GigaScience","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":211,"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":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Compute Canada; Genome Canada; Canarie","keywords":"Computer science; Workflow; Scalability; Cloud computing; Genomics; MIT License; Metagenomics; Python (programming language); Software; Software deployment; Data science; Computational biology; Distributed computing; Software engineering; Database; Genome; Operating system; Biology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002118581,0.0001178581,0.0001458656,0.00001966884,0.0001763233,0.0001219479,0.0004944291,0.00007221862,0.000009636277],"category_scores_gemma":[0.00006691452,0.0001050881,0.00003206616,0.00009602901,0.0001222269,0.00000323548,0.0003987429,0.00003742748,0.000006332771],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007805676,"about_ca_system_score_gemma":0.00005504729,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003584203,"about_ca_topic_score_gemma":0.00001537147,"domain_scores_codex":[0.9990398,0.00001979101,0.0001241655,0.0004871382,0.00006796105,0.0002611475],"domain_scores_gemma":[0.9993816,0.00002445963,0.00005984709,0.0003796321,0.0000601268,0.00009433694],"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":[0.0000294451,0.00002578567,0.01224227,0.000008748171,0.00002128424,1.295188e-7,0.00006577718,0.0004366353,0.9847586,0.0005146909,0.0001497311,0.0017469],"study_design_scores_gemma":[0.002239375,0.003594715,0.2659039,0.00005176028,0.0001169644,0.00003490758,0.001606054,0.005793869,0.509896,0.01350387,0.1957948,0.001463747],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9727919,0.0009932636,0.02541066,0.00008421903,0.0001102325,0.0003468509,0.00007331633,0.000003738707,0.0001858824],"genre_scores_gemma":[0.9823857,0.0001426091,0.01667055,0.0003236557,0.00007366388,0.00002890592,0.00003425582,0.00001275974,0.0003278824],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4748626,"threshold_uncertainty_score":0.4285367,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0323129437284491,"score_gpt":0.3186136926475547,"score_spread":0.2863007489191056,"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."}}