Bibliographic record
Abstract
Arturo Macchi of the University of Ottawa and Dominique M. Roberge of Lonza summarized (Org. Process Res. Dev. 2014, 18, 1286) a “toolbox approach” for the evolution from batch to continuous chemical synthesis. Michael D. Organ of York University developed (Org. Process Res. Dev. 2014, 18, 1315) a flow reactor with inline analytics, and Timothy D. White of Eli Lilly described (Org. Process Res. Dev. 2014, 18, 1482) the continuous production of solid products under flow conditions. Electrochemical reduction and oxidation are particularly easy under flow conditions. Steven V. Ley of the University of Cambridge oxidized (Org. Lett. 2014, 16, 4618) 1 under flow conditions, then condensed the product with tryptamine 2 to prepare the indole alkaloid Nazlinine 3. Thomas Wirth of Cardiff University electrolyzed (Org. Process Res. Dev. 2014, 18, 1377) the carbonate 4 in a non-divided cell to return the deprotected phenol 5. Timothy Noël of the Eindhoven University of Technology gathered (Chem. Eur. J. 2014, 20, 10562) an overview of photochemical transformations under flow conditions. Kevin I. Booker-Milburn of the University of Bristol observed (Chem. Eur. J. 2014, 20, 15226) superior yields for the coupling of 6 with 7 to form 8 under flow compared to batch conditions. Koichi Fukase of Osaka University and Ilhyong Ryu of Osaka Prefecture University converted (Chem. Eur. J. 2014, 20, 12750) 9 selectively to 10 under flow conditions. Alexei A. Lapkin, also of the University of Cambridge, optimized (Org. Process Res. Dev. 2014, 18, 1443) the singlet oxygen conversion of 11 to 12. Shawn K. Collins of the Université de Montréal cyclized (Org. Process Res. Dev. 2014, 18, 1571) 13 to 14. There have been several advances in the use of enzymes under flow conditions. Rodrigo O. M. A. de Souza of the Federal University of Rio de Janeiro found (Org. Process Res. Dev. 2014, 18, 1372) that lipase in a microemulsion-based organogel efficiently converted coupled 15 with 16 to make 17. Timothy F. Jamison of MIT developed (Org. Lett. 2014, 16, 6092) a catch-and-release protocol for the reductive amination of 18 with 19 to give 20.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.248 | 0.141 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".