Bibliographic record
Abstract
Carbonyls (C=O groups) are popular reactive handles. But carbonylation reactions, which create these useful groups by appending carbon monoxide to aryl halides or alkyl halides with the help of a metal catalyst, tend to work with only a few types of molecules. So even though it’s possible to make ibuprofen on an industrial scale using carbonylation, the reaction isn’t a common tool. But that could change, thanks to a discovery by McGill University chemists Bruce A. Arndtsen, Gerardo M. Torres, and Yi Liu. These researchers found that by shining blue light on a palladium-catalyzed carbonylation, they could make the reaction work with many types of aryl halides and alkyl halides. The transformation can be used to make acid chlorides, amides (example shown), esters, and ketones, motifs found in or en route to a large number of pharmaceuticals (Science 2020, DOI: 10.1126/science.aba5901). Ilhyong Ryu, a chemist at Osaka Prefecture University who
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.011 |
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".