{"id":"W2934659072","doi":"10.1039/c9re00086k","title":"Development of an automated kinetic profiling system with online HPLC for reaction optimization","year":2019,"lang":"en","type":"article","venue":"Reaction Chemistry & Engineering","topic":"Analytical Chemistry and Chromatography","field":"Chemistry","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vancouver Biotech (Canada); University of British Columbia","funders":"University of British Columbia; Merck","keywords":"Profiling (computer programming); High-performance liquid chromatography; Computer science; Chromatography; Chemistry; Process engineering; Engineering; 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":[0.002184378,0.001803464,0.001380276,0.002008111,0.0007634276,0.001200955,0.001792958,0.001045033,0.003159031],"category_scores_gemma":[0.002555858,0.001132833,0.0005446518,0.001357744,0.0004372446,0.001277153,0.0007173198,0.001586667,0.003373179],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006776765,"about_ca_system_score_gemma":0.00220693,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00154731,"about_ca_topic_score_gemma":0.002280192,"domain_scores_codex":[0.9969193,0.0004818444,0.0002857241,0.0005852609,0.001542412,0.0001855183],"domain_scores_gemma":[0.9981862,0.0005723379,0.0002487575,0.000199401,0.0006645216,0.0001286254],"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":[0.0003422543,0.0005110718,0.001245101,0.0002341446,0.0000576034,0.00007916443,0.00006476821,0.001846455,0.906547,0.001022284,0.002375089,0.08567511],"study_design_scores_gemma":[0.00007021749,0.0005227571,0.00191261,0.00002521302,0.00007870783,0.0003006038,0.00001953893,0.04280275,0.9398918,0.0004059791,0.01385795,0.0001118952],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05826685,0.00087769,0.9174544,0.0002942112,0.0002002141,0.001545215,0.001754014,0.01611274,0.003494673],"genre_scores_gemma":[0.1403787,0.001034611,0.8452721,0.0004266272,0.0001553768,0.002703488,0.002795781,0.001388613,0.005844665],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003159031,"threshold_uncertainty_score":0.01155221,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006605233289022229,"score_gpt":0.2179955533010928,"score_spread":0.2113903200120706,"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."}}