{"id":"W2136207878","doi":"10.1093/bioinformatics/btu595","title":"BigDataScript: a scripting language for data pipelines","year":2014,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University and Génome Québec Innovation Centre","funders":"National Institute of Diabetes and Digestive and Kidney Diseases; Canadian Institutes of Health Research; National Institutes of Health; Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Computer science; Pipeline transport; Software portability; Serialization; Scripting language; Programming language; Debugging; Robustness (evolution); Python (programming language); Operating system","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":[0.005825674,0.002204516,0.0008824196,0.001056353,0.0008553524,0.002465875,0.00393031,0.001287973,0.01153207],"category_scores_gemma":[0.01377116,0.002187507,0.001744396,0.001379434,0.002269281,0.00417652,0.003599876,0.004841645,0.01287297],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008496265,"about_ca_system_score_gemma":0.003169929,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001344155,"about_ca_topic_score_gemma":0.001149894,"domain_scores_codex":[0.9967451,0.0005960265,0.0006566661,0.0008508376,0.0009380566,0.0002131638],"domain_scores_gemma":[0.9910945,0.003860881,0.0008044061,0.001880694,0.001609026,0.0007505745],"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":[0.003042469,0.0004655034,0.009459035,0.003998316,0.0002926396,0.001798126,0.002539568,0.02870402,0.1155103,0.07432544,0.4429023,0.3169623],"study_design_scores_gemma":[0.0005479055,0.0002583843,0.002840267,0.0005745577,0.0001046514,0.001578967,0.0001696102,0.1868938,0.16936,0.094956,0.5423629,0.0003529337],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.001729316,0.0001129804,0.7905281,0.000325912,0.0001288041,0.0003125886,0.005076105,0.1996168,0.002169326],"genre_scores_gemma":[0.03597399,0.0003847932,0.8528669,0.001259613,0.0001410938,0.00140988,0.02082895,0.08168312,0.005451636],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.01153207,"threshold_uncertainty_score":0.03857863,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03272703732389456,"score_gpt":0.2770277309802032,"score_spread":0.2443006936563086,"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."}}