Bibliographic record
Abstract
James A. Bull of Imperial College London prepared (J. Org. Chem. 2013, 78, 6632) the aziridine 2 with high diastereocontrol by adding the anion of diiodomethane to the imine 1. Karl Anker Jørgensen of Aarhus University observed (Chem. Commun. 2013, 49, 6382) high ee in the distal aziridination of 3 to give 4. Benito Alcaide of the Universidad Complutense de Madrid and Pedro Almendros of ICOQ- CSIC Madrid reduced (Adv. Synth. Catal. 2013, 355, 2089) the β-lactam 5 to the azetidine 6. Hiroaki Sasai of Osaka University added (Org. Lett. 2013, 15, 4142) the allenoate 8 to the imine 7, delivering the azetidine 9 in high ee. Tamio Hayashi of Kyoto University, the National University of Singapore, and A*STAR devised (J. Am. Chem. Soc. 2013, 135, 10990) a Pd catalyst for the enantioselective addition of the areneboronic acid 11 to the pyrroline 10 to give 12. Ryan A. Brawn of Pfizer (Org. Lett. 2013, 15, 3424) reported related results. Nicolai Cramer of the Ecole Polytechnique Fédérale de Lausanne developed (J. Am. Chem. Soc. 2013, 135, 11772) a Ni catalyst for the cyclization of the formamide 13 to the lactam 14. Andrew D. Smith of the University of St. Andrews used (Org. Lett. 2013, 15, 3472) an organocatalyst to cyclize 15 to 16. Jose L. Vicario of the Universidad del Pais Vasco effected (Synthesis 2013, 45, 2669) the multicomponent coupling of 17, 18, and 19, mediated by an organocatalyst, to construct 20 in high ee. André Beauchemin of the University of Ottawa explored (J. Org. Chem. 2013, 78, 12735) the thermal cyclization of ω-alkenyl hydroxyl amines such as 21. Abigail G. Doyle of Princeton University developed (Angew. Chem. Int. Ed. 2013, 52, 9153) a Ni catalyst for the enantioselective addition of aryl zinc bromides such as 24 to the prochiral 23, to give 25 in high ee. Dennis G. Hall of the University of Alberta developed (Angew. Chem. Int. Ed. 2013, 52, 8069) an in situ preparation of the allyl boronate 26 in high ee. Addition to the aldehyde 27 proceeded with high diasteroselectivity.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".