{"id":"W4392155264","doi":"10.1021/acs.oprd.3c00392","title":"Doing More with Less, On Time and In Full: An Intelligent Multiattribute Process Optimization Platform","year":2024,"lang":"en","type":"article","venue":"Organic Process Research & Development","topic":"Innovative Microfluidic and Catalytic Techniques Innovation","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Workflow; Analytics; Process (computing); Process engineering; Computer science; Yield (engineering); Process optimization; Data mining; Engineering; Materials science; Database; Chemical 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001499469,0.001019558,0.0007853433,0.0006748484,0.0004500058,0.00212077,0.001496404,0.0007018677,0.002293369],"category_scores_gemma":[0.0008727791,0.0005101081,0.0006398149,0.000416666,0.0006371891,0.001347887,0.001618449,0.001318871,0.001681534],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005044828,"about_ca_system_score_gemma":0.001317931,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004274535,"about_ca_topic_score_gemma":0.0004782111,"domain_scores_codex":[0.9988399,0.0001267552,0.00005764188,0.0003301423,0.0005393833,0.0001061874],"domain_scores_gemma":[0.9994662,0.00009153371,0.00009770846,0.0001774422,0.00009652335,0.00007061853],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007438378,0.0006757627,0.001513162,0.0002147176,0.0001068464,0.0002144117,0.0001421044,0.04537317,0.7779896,0.008329154,0.002636571,0.1620607],"study_design_scores_gemma":[0.0001139623,0.0009645563,0.001729545,0.000031748,0.00008091773,0.0003043961,0.00003749301,0.3791194,0.5841993,0.00793433,0.02532629,0.0001580156],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05208047,0.000241237,0.9322814,0.0002678485,0.00006547155,0.0002805222,0.0002229731,0.01080303,0.003757065],"genre_scores_gemma":[0.2686261,0.0002850441,0.7246028,0.000269136,0.00004066296,0.0003907335,0.0006116205,0.0007569888,0.004416943],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002293369,"threshold_uncertainty_score":0.0079301,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02920078371352814,"score_gpt":0.3178161591390518,"score_spread":0.2886153754255237,"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."}}