{"id":"W2313878851","doi":"10.1021/op5002512","title":"A Flow Reactor with Inline Analytics: Design and Implementation","year":2014,"lang":"en","type":"article","venue":"Organic Process Research & Development","topic":"Innovative Microfluidic and Catalytic Techniques Innovation","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Eli Lilly and Company","keywords":"Analytics; Process (computing); Process engineering; Continuous flow; Flow (mathematics); Computer science; Chemistry; Engineering; Physics; Biochemical engineering; Mechanics; Database; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001466794,0.0001448259,0.0001357399,0.0002801091,0.0001234846,0.00006260408,0.0001520874,0.00005647333,0.00009238346],"category_scores_gemma":[0.00006004005,0.0001214785,0.000004894316,0.001026059,0.00007121346,0.0001639625,0.00005238824,0.0002540958,0.00002250719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002461793,"about_ca_system_score_gemma":0.0002501386,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002174374,"about_ca_topic_score_gemma":0.000002415706,"domain_scores_codex":[0.9986728,0.00003812516,0.0002663951,0.0002273933,0.0004386924,0.0003566061],"domain_scores_gemma":[0.9992156,0.00004978407,0.00003021905,0.000148436,0.000496985,0.00005898726],"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":[0.00007868708,0.00007287904,0.001217597,0.0006072801,0.0001911351,0.00000865055,0.004954178,0.0000904914,0.55801,0.002977086,0.006204756,0.4255872],"study_design_scores_gemma":[0.0004396431,0.0001208391,0.001273427,0.00006483905,0.000005061342,0.00001211941,0.0004032999,0.007165565,0.9794783,0.0008166877,0.009992804,0.0002274068],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1112995,0.000070911,0.8873411,0.0001140849,0.00001860627,0.0004265406,0.000001681656,0.0002097965,0.000517795],"genre_scores_gemma":[0.9607551,0.00004184014,0.03884993,0.00003978632,0.00003711898,0.0001159447,0.00006309153,0.00003987859,0.00005736071],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8494555,"threshold_uncertainty_score":0.4953749,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0297143037915797,"score_gpt":0.3213487949344405,"score_spread":0.2916344911428608,"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."}}