{"id":"W3035851635","doi":"10.1371/journal.pcbi.1007942","title":"Moving analytical ultracentrifugation software to a good manufacturing practices (GMP) environment","year":2020,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"Protein purification and stability","field":"Biochemistry, Genetics and Molecular Biology","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Lethbridge","funders":"Canadian Institutes of Health Research; National Institutes of Health; National Institute of General Medical Sciences; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Analytical Ultracentrifugation; Software; Computer science; Workflow; Biopharmaceutical; Instrumentation (computer programming); Software engineering; Ultracentrifuge; Chemistry","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":[0.0001090931,0.0001167093,0.0001181717,0.00002643443,0.00008126319,0.00002007553,0.0001505556,0.0001027314,0.00008756981],"category_scores_gemma":[0.000696826,0.0001154225,0.00005060757,0.00004793189,0.00004867834,0.000006326303,0.0000922724,0.00009255139,0.00009154705],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002414031,"about_ca_system_score_gemma":0.00005063635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003111259,"about_ca_topic_score_gemma":8.948522e-7,"domain_scores_codex":[0.9989511,0.00008862467,0.0002243596,0.0004477382,0.0001188033,0.0001693751],"domain_scores_gemma":[0.9994273,0.00007837326,0.0001525457,0.0001377919,0.00004638648,0.00015763],"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":[0.0007674909,0.0009498227,0.05360222,0.0001308874,0.0004732329,0.000005661936,0.0005693278,0.09348683,0.8165857,0.005959502,0.0005265031,0.02694281],"study_design_scores_gemma":[0.0029134,0.002245754,0.1379734,0.00002947201,0.0001479043,0.00003788579,0.0003137349,0.06688531,0.50229,0.003449251,0.2821779,0.001535947],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7039047,0.000161842,0.2881819,0.007046837,0.00003758834,0.0003908132,0.00006863513,0.00004000579,0.0001676321],"genre_scores_gemma":[0.9712723,0.00001101576,0.0263236,0.001701343,0.0001612494,0.00003479595,0.0004636981,0.000009855391,0.00002209221],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3142957,"threshold_uncertainty_score":0.4706795,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03105539678500368,"score_gpt":0.2758552806157006,"score_spread":0.2447998838306969,"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."}}