{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003115658,0.0001152222,0.0001156024,0.00002383286,0.00009706368,0.00003772396,0.0004067832,0.00006016982,0.000002068784],"category_scores_gemma":[0.000318687,0.0001000817,0.00003913958,0.00003543188,0.00002965559,0.000002219598,0.0003643917,0.00002760341,0.00001192934],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000002888502,"about_ca_system_score_gemma":0.00002354474,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001033648,"about_ca_topic_score_gemma":0.00002579291,"domain_scores_codex":[0.9993268,0.000009792899,0.0002364936,0.0001579061,0.00006661859,0.0002024305],"domain_scores_gemma":[0.9990789,0.00001714526,0.00008749848,0.0007149641,0.00005519075,0.00004625356],"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":[0.0001142438,0.000101895,0.002989759,0.0005552256,0.0002929761,5.161374e-7,0.001590107,0.000115077,0.4696582,0.001280175,0.2652274,0.2580745],"study_design_scores_gemma":[0.0006677944,0.000178823,0.0003404306,0.00001308849,0.00003872261,0.000007293111,0.00075078,0.05496251,0.01468465,0.00007921188,0.9280015,0.0002751734],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6566678,0.004148602,0.32422,0.000640135,0.001759251,0.001156497,0.002595907,0.00004532221,0.008766424],"genre_scores_gemma":[0.7685835,0.0002642155,0.2236873,0.001878799,0.001300325,0.00003542464,0.003445056,0.00004234807,0.0007630861],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6627741,"threshold_uncertainty_score":0.4081215,"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."}}