{"id":"W4413399315","doi":"10.1021/acs.oprd.5c00230","title":"A Continuous Manufacturing Line Generating Organozinc Species in Flow: Enhancing the Simmons-Smith Reaction Including Post-Reaction Processing","year":2025,"lang":"en","type":"article","venue":"Organic Process Research & Development","topic":"Innovative Microfluidic and Catalytic Techniques Innovation","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Particle Physics","funders":"Steirische Wirtschaftsförderungsgesellschaft; Österreichische Forschungsförderungsgesellschaft; Bundesministerium für Klimaschutz, Umwelt, Energie, Mobilität, Innovation und Technologie; Bundesministerium für Arbeit und Wirtschaft","keywords":"Continuous flow; Flow chemistry; Chemistry; Line (geometry); Process engineering; Computer science; Combinatorial chemistry; Biochemical engineering; Mathematics; Engineering","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0007541325,0.0009339889,0.0003601994,0.0007528157,0.0003289641,0.0004786222,0.0005277809,0.0005580526,0.004801039],"category_scores_gemma":[0.000416909,0.0003941408,0.0003876511,0.0003870986,0.000393813,0.0005579431,0.0003360078,0.001039915,0.003266701],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003073921,"about_ca_system_score_gemma":0.0005262618,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002866283,"about_ca_topic_score_gemma":0.0006533179,"domain_scores_codex":[0.9995688,0.00004772253,0.00002547341,0.0001421465,0.0001667048,0.00004920122],"domain_scores_gemma":[0.9996818,0.00007979877,0.00008434588,0.0000670236,0.00005739977,0.00002952293],"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":[0.00005758506,0.00003449502,0.00009838898,0.00004952823,0.000004839827,0.00005292251,0.00002064045,0.00006085966,0.9922159,0.0002822048,0.0002850424,0.006837529],"study_design_scores_gemma":[0.000008566663,0.0001129562,0.0003427432,0.000003090709,0.000006332519,0.0001094652,0.000005028412,0.0006183423,0.9940932,0.00005124409,0.004641932,0.000007200774],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.350292,0.001507145,0.6290832,0.0004186534,0.0002807711,0.000920516,0.001666451,0.006303929,0.009527373],"genre_scores_gemma":[0.4721807,0.001717688,0.5054131,0.0002507289,0.0001382737,0.0009910471,0.002156742,0.0007451516,0.01640661],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004801039,"threshold_uncertainty_score":0.01606107,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02637384418141672,"score_gpt":0.3098697683825875,"score_spread":0.2834959242011708,"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."}}