Bibliographic record
Abstract
Alois Fürstner of the Max-Planck-Institut Mülheim devised (Angew. Chem. Int. Ed. 2013, 52, 14050) a Ru catalyst for the trans- selective hydroboration of an alkyne 1 to 2. Qingbin Liu of Hebei Normal University and Chanjuan Xi of Tsinghua University coupled (Org. Lett. 2013, 15, 5174) the alkenyl zirconocene derived from 3 with an acyl azide to give the amide 4. Chulbom Lee of Seoul National University used (Angew. Chem. Int. Ed. 2013, 52, 10023) a Rh catalyst to convert a terminal alkyne 5 to the ester 6. Laura L. Anderson of the University of Illinois, Chicago devised (Org. Lett. 2013, 15, 4830) a protocol for the conversion of a terminal alkyne 7 to the α-amino aldehyde 9. Dewen Dong of the Changchun Institute of Applied Chemistry developed (J. Org. Chem. 2013, 78, 11956) conditions for the monohydrolysis of a bis nitrile 10 to the monoamide 11. Aiwen Lei of Wuhan University optimized (Chem. Commun. 2013, 49, 7923) a Ni catalyst for the conversion of the alkene 12 to the enamide 13. Kazushi Mashima of Osaka University optimized (Adv. Synth. Catal. 2013, 355, 3391) a boronic ester catalyst for the conversion of an amide 14 to the ester 15. Jean- François Paquin of the Université Laval prepared (Eur. J. Org. Chem. 2013, 4325) the amide 17 by coupling an amine with the activated intermediate from reaction of an acid 16 with Xtal- Fluor E. Steven Fletcher of the University of Maryland School of Pharmacy designed (Tetrahedron Lett. 2013, 54, 4624) the azodicarbonyl dimorpholide 18 as a reagent for the Mitsunobu coupling of 19 with 20. The reduced form of 18 was readily separated by extraction into water and reoxidized. Jens Deutsch of the Universität Rostock found (Chem. Eur. J. 2013, 19, 17702) simple ligands for the Ru-mediated borrowed hydrogen conversion of an alcohol 22 to the amine 23. Ronald T. Raines of the University of Wisconsin devised (J. Am. Chem. Soc. 2013, 135, 14936) a phosphinoester for the efficient conversion in water of an azide 24 to the diazo 25.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 0.013 |
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".