Bibliographic record
Abstract
Introduction Modern asymmetric synthesis relies on new and improved catalytic transformations. Understanding the balance of steric and electronic factors is a prerequisite to fine-tuning a catalyst to achieve optimal selectivity in a particular reaction. Among the chiral ligands developed so far, BINOL 1 and related molecules with axial chirality have found wide utility in asymmetric catalysis ( 1 ). 1 1. BINOL High Resolution Image Download MS PowerPoint Slide BINOL was first synthesized in 1926 ( 2 ), however, its potential as a ligand for metal-mediated catalysis was left unrecognized until 1979 when Noyori demonstrated its utility in the reduction of aromatic ketones and aldehydes ( 3 ). Since Noyori's discovery, many modifications of the BINOL skeleton aimed at changing its steric and electronic properties have been reported ( 4 ). For example, partially hydrogenated BINOL was used in enantioselective alkylation of aldehydes ( 4a ); conjugate addition of diethylzinc to cyclic enones ( 4b ); and ring opening of epoxides ( 4c ). Selective bromination at the 6 and 6' positions of the
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".