{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002247822,0.001582985,0.001268578,0.001161168,0.0007825426,0.001463594,0.003332413,0.001532103,0.007119463],"category_scores_gemma":[0.001117798,0.001144322,0.0004317167,0.0005220601,0.0005357665,0.00109763,0.0008556719,0.00128091,0.004671501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008894975,"about_ca_system_score_gemma":0.001751809,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009438786,"about_ca_topic_score_gemma":0.0008955816,"domain_scores_codex":[0.998585,0.0001907757,0.00009799468,0.0003660237,0.0006281385,0.0001320638],"domain_scores_gemma":[0.9991776,0.0001356086,0.0001566745,0.00011697,0.0002829921,0.0001301099],"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":[0.0008188153,0.0008751379,0.001618319,0.0009672402,0.00006963452,0.0001997207,0.0001842011,0.00248307,0.8906001,0.003106817,0.00502263,0.09405436],"study_design_scores_gemma":[0.0002613328,0.003201046,0.002602235,0.00005999394,0.00009509976,0.0008090065,0.00004769551,0.03278188,0.8987558,0.0004991594,0.06074055,0.0001461625],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07099743,0.001255378,0.898504,0.0004457728,0.0005044115,0.004941979,0.002244458,0.01676473,0.004341787],"genre_scores_gemma":[0.1567115,0.00101381,0.8249203,0.000479789,0.0002411226,0.004999742,0.001680426,0.0007795921,0.009173769],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007119463,"threshold_uncertainty_score":0.023817,"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."}}