{"id":"W2946936700","doi":"10.1016/j.mec.2019.e00089","title":"Impact framework: A python package for writing data analysis workflows to interpret microbial physiology","year":2019,"lang":"en","type":"article","venue":"Metabolic Engineering Communications","topic":"Microbial Metabolic Engineering and Bioproduction","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Ontario; Genome Canada","keywords":"Workflow; Python (programming language); Computer science; Bottleneck; Visualization; Data science; Software engineering; Interoperability; Automation; Data mining; Programming language; Database; World Wide Web; Engineering","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":[0.0004292565,0.000238791,0.0003912617,0.0002458325,0.00008230454,0.0000495272,0.001436574,0.000166763,0.00002237901],"category_scores_gemma":[0.0005049839,0.0002392577,0.0002326819,0.0007017751,0.00002489651,0.00001354151,0.0008145106,0.0002188442,0.00002616806],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001642885,"about_ca_system_score_gemma":0.00003672869,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003974957,"about_ca_topic_score_gemma":0.00001807709,"domain_scores_codex":[0.9986437,0.00005685975,0.0003277979,0.0005414612,0.00006642946,0.0003637552],"domain_scores_gemma":[0.995459,0.00005031914,0.00005754348,0.004229416,0.0001002286,0.0001035589],"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":[0.00002359742,0.00005266455,0.0003837654,0.00001857766,0.000643628,3.530058e-8,0.00009533602,0.01063948,0.9853855,0.0003267794,0.0004513531,0.001979253],"study_design_scores_gemma":[0.001214554,0.0003438905,0.06081561,0.0001910035,0.001772955,0.00002548475,0.0001104692,0.04559108,0.2026345,0.00006127765,0.6853538,0.001885407],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6956756,0.003060707,0.299424,0.0002919856,0.0004329461,0.0005529624,0.0004345577,0.0000926853,0.00003456288],"genre_scores_gemma":[0.8864292,0.0004225098,0.1106179,0.00009475653,0.0004219713,0.00006870116,0.00179027,0.00005007231,0.000104663],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7827511,"threshold_uncertainty_score":0.9756646,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0183018291238267,"score_gpt":0.3144582303960871,"score_spread":0.2961564012722604,"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."}}