{"id":"W2804000556","doi":"10.1002/cptc.201800096","title":"Exploiting Photochemical Processes in Multi‐Step Continuous Flow: Derivatization of the Natural Product Clausine C","year":2018,"lang":"en","type":"article","venue":"ChemPhotoChem","topic":"Radical Photochemical Reactions","field":"Chemistry","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Centre in Green Chemistry and Catalysis","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Centre in Green Chemistry and Catalysis","keywords":"Derivatization; Yield (engineering); Continuous flow; Chemistry; Flow chemistry; Natural product; Catalysis; Combinatorial chemistry; Photochemistry; Organic chemistry; Materials science; High-performance liquid chromatography; Biochemical 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":[0.0003061917,0.0004636752,0.000239261,0.0002905088,0.0002291003,0.0003988793,0.0004209934,0.0005040428,0.001289548],"category_scores_gemma":[0.0002874232,0.0002441586,0.0002510853,0.0002358051,0.0004048808,0.000503112,0.0003243762,0.0009560133,0.0003266388],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004611656,"about_ca_system_score_gemma":0.0003036808,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001107084,"about_ca_topic_score_gemma":0.00138159,"domain_scores_codex":[0.9998237,0.00002106374,0.000005911678,0.00006070427,0.00005144845,0.00003702315],"domain_scores_gemma":[0.9998523,0.00005298812,0.00003503236,0.00002067558,0.00001893681,0.00001998953],"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":[0.00008926151,0.00005362874,0.00007840389,0.00006031039,0.000007437335,0.00007080034,0.00003037561,0.0002214889,0.9949091,0.0002215457,0.0000594196,0.004198175],"study_design_scores_gemma":[0.00002014076,0.0001375009,0.0003707765,0.000002015213,0.000005384299,0.000065089,0.000005615254,0.000787343,0.9975581,0.00004158896,0.0009983528,0.000008175735],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9574244,0.001761028,0.03674571,0.0001553149,0.00006892629,0.0001312553,0.0002117125,0.0005099277,0.002991626],"genre_scores_gemma":[0.9834338,0.0003941357,0.01471752,0.00003875306,0.00001357098,0.00003933249,0.00007561872,0.00002751866,0.001259723],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001289548,"threshold_uncertainty_score":0.004313946,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01782727667107961,"score_gpt":0.2619407422344007,"score_spread":0.2441134655633211,"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."}}