Reaction Mechanisms for Chiral-Phosphate-Catalyzed Transformations Involving Cationic Intermediates and Protic Nucleophiles
Bibliographic record
Abstract
Abstract Recent strategies for enantioinduction often focus on employing a chiral catalyst to noncovalently interact with the substrate. By restricting the number of low energy diastereomeric transition states the reacting components can adopt, stereoselectivity can be achieved. Many of these noncovalent interactions include a significant dispersive component and these types of contacts have historically been difficult to model accurately. Modern computational methods have been designed to overcome such limitations. Using our computational work on chiral phosphate catalysis, we discuss the reasons for enantioselectivity in diverse reaction space. 1 Introduction 2 Chiral Phosphate Catalysis 3 Phosphate-Catalyzed Transfer Hydrogenation 4 Phosphate-Catalyzed Aza-Friedel–Crafts Reaction 5 Phosphate-Catalyzed Reactions Involving Allenamides 6 Comprehensive Qualitative Models 7 Chiral Phosphates and Thionium Intermediates 8 Conclusion
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".