{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008962177,0.0002080939,0.0001644392,0.000605803,0.0001252688,0.0001541157,0.0002077902,0.0001057393,0.00007210739],"category_scores_gemma":[0.0000355554,0.0001756737,0.000006158626,0.002011302,0.00008475204,0.0004505722,0.00005771947,0.000545152,0.00003045945],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000557025,"about_ca_system_score_gemma":0.0004194598,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002610331,"about_ca_topic_score_gemma":0.000004775283,"domain_scores_codex":[0.998296,0.00001439659,0.0003301229,0.0003845208,0.0005398732,0.0004350685],"domain_scores_gemma":[0.9993899,0.0000341347,0.00002052215,0.0001456994,0.0003441194,0.00006563741],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007493764,0.0006451596,0.0009502317,0.00718582,0.0004042987,0.0003970512,0.04853157,0.05915196,0.2829598,0.00249993,0.001600869,0.594924],"study_design_scores_gemma":[0.0002962347,0.0001594826,0.0002327572,0.001090253,0.000003991632,0.0000377221,0.001276347,0.07039651,0.92414,0.0002051813,0.001736114,0.0004254574],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6683656,0.00053661,0.3286007,0.0001718661,0.000047663,0.0008089946,0.000008621546,0.0006625614,0.0007973696],"genre_scores_gemma":[0.9938297,0.0001024787,0.005518268,0.00002396793,0.0000299744,0.0001556845,0.0001749667,0.00007196044,0.00009305332],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6411802,"threshold_uncertainty_score":0.7163764,"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."}}