Formation of Sugar-Specific Reactive Intermediates from <sup>13</sup>C-Labeled <scp>l</scp>-Serines
Bibliographic record
Abstract
Analysis of the pyrolysis products of [1-(13)C], [2-(13)C], and [3-(13)C]-labeled L-serines has indicated the presence of three initial degradation pathways. Decarboxylation followed by deamination produces aminoethanol and acetaldehyde, respectively; a retro-aldol pathway generates formaldehyde and glycine. Dehydration of L-serine can lead to the formation of pyruvic acid, which eventually can be converted into the amino acid alanine. Formation of alanine and glycine was confirmed due to the detection of 2, 5-diketo-3,6-dimethylpiperazine and cycloglycylalanine. Most of the advanced decomposition products of L-serine can be rationalized on the basis of these initial degradation products. Label incorporation studies have elucidated the origin of carbonyl precursors of methyl- and 2,3-dimethylpyrazines formed in the thermal decomposition mixture of L-serine. Three mechanistic pathways were identified for the formation of carbonyl precursors of methyl- and 2, 3-dimethylpyrazines. The major pathway (70%) for the formation of the precursor of methylpyrazine involved aldol addition of formaldehyde to glycolaldehyde to form glyceraldehyde. On the other hand, the major pathway (60%) for the formation of the precursor of 2,3-dimethylpyrazine involved an aldol condensation of acetaldehyde with glycolaldehyde to form 2,3-butanedione.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".