On the Origin of Sugar Handedness: Facts, Hypotheses and Missing Links-A Review
Bibliographic record
Abstract
By paraphrasing one of Kipling's most amazing short stories (How the Leopard Got His Spots), this article could be entitled "How Sugars Became Homochiral". Obviously, we have no answer to this still unsolved mystery, and this perspective simply brings recent models, experiments and hypotheses into the homochiral homogeneity of sugars on earth. We shall revisit the past and current understanding of sugar chirality in the context of prebiotic chemistry, with attention to recent developments and insights. Different scenarios and pathways will be discussed, from the widely known formose-type processes to less familiar ones, often viewed as unorthodox chemical routes. In particular, problems associated with the spontaneous generation of enantiomeric imbalances and the transfer of chirality will be tackled. As carbohydrates are essential components of all cellular systems, astrochemical and terrestrial observations suggest that saccharides originated from environmentally available feedstocks. Such substances would have been capable of sustaining autotrophic and heterotrophic mechanisms integrating nutrients, metabolism and the genome after compartmentalization. Recent findings likewise indicate that sugars' enantiomeric bias may have emerged by a transfer of chirality mechanisms, rather than by deracemization of sugar backbones, yet providing an evolutionary advantage that fueled the cellular machinery.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".