Bibliographic record
Abstract
For more than a century, organic chemists have been playing in Nature's laboratory. Their first goal was to understand the organization of atoms in the living matter and then to reproduce it by synthesis. This quest gave rise to several efficient techniques to synthesise molecules; many of them still in use nowadays, as such or with little modifications. Even at the beginning of this journey, the chemists discovered that their methods were far from being as efficient as the ones used by Nature to produce substances. The natural molecules were chiral and there was even an enantiomer that was produced over the other;a lesson of perfection. This was another challenge for the chemists and they succeeded by first developing techniques to separate enantiomers and more recently reagents and reactions to produce only the desired stereoisomer. Asymmetric synthesis uses chiral auxiliaries, reagents or catalysts to create chirality into the desired compound. The common perception, as a minimum condition, was that the chiral substance used to perform such a transformation has to be of the highest enantiomeric purity to obtain a very high selectivity. The relation between the enantiomeric excesses of the chiral substance and the product was suggested to be linear. But there were a lot of surprises left in the laboratory. Who would have thought that an impure substance could give an enantiomeric excess in the product higher than its own purity? The molecules are acting in different ways in solution. Self-organization and aggregation can arise depending on the structure of the substance or its environment. Such phenomenon can generate deviations to the awaited behaviour of the molecules that can be observed in many cases. This article tries to present some examples of the historical reports of such peculiar behaviours, their influence on physico-chemical properties and the final discovery of the now well-known nonlinear effects in asymmetric synthesis.Key words: asymmetric synthesis, diastereomeric interactions, nonlinear effects.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".