Thieme Cheminar: Boron in Organic Synthesis
Bibliographic record
Abstract
Join us for the Thieme Cheminar “Boron in Organic Synthesis” on Thursday, October 16, 2025, at 3:00 PM (CET) This special online event, brought to you by SynOpen Journal, highlights the versatile role of boron in organic synthesis and its importance in advancing organic chemistry. The program will feature outstanding talks from leading researchers in the field: 🔹Prof. Varinder Aggarwal (University of Bristol, UK) 🔹Prof. Andrei Yudin (University of Toronto, Canada) 🔹Prof. Benjamin J. Stokes (Santa Clara University, USA) The session will be chaired by Prof. Thierry Ollevier, Editor-in-Chief of SynOpen. Do not miss this unique opportunity to explore cutting-edge advances in boron chemistry and engage with some of the leading voices defining tomorrow’s organic chemistry! Boron mediated synthesis of tetrasubstituted alkenes Talk by Varinder Aggarwal, Thierry Ollevier Advances in boron chemistry: reagents, mechanisms, and targets Talk by Andrei Yudin Tetrahydroxydiboron in Pd-Catalyzed Transfer Hydrogenations Talk by Ben Stokes
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.271 | 0.185 |
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".