Changes in the levels of indoleamine phytochemicals during véraison and ripening of wine grapes
Bibliographic record
Abstract
Melatonin and serotonin have previously been described in mature wine grapes and finished wines, but the metabolism of these signalling molecules in the development of wine grapes has not previously been investigated. We harvested wine grapes at different stages of development from lag phase through véraison from eight different commercial vineyards representing a diversity of growing conditions, management practices, merlot varietals and localized ecosystems to determine whether different patterns in melatonin and serotonin can be found in wine grapes during seed development and berry maturation. Melatonin was detected in 45% of the fully developed purple, postvéraison grapes but only found in 23% of prelag phase samples. However, the actual concentration of melatonin was highest in wine grapes harvested at the early stage of véraison when the seed is developing. Serotonin was not detected in any of the prelag phase grapes but was consistently detected in 30-35% of grapes harvested during the véraison transition at consistent levels of about 8-10 mug/g. Interestingly, the nitrogen storage compound gamma-aminobutyric acid was also found at about 115 mug/g in 77% of early stage green grapes and declined in both prevalence and concentration with ripening. Together, these data are indicative of a potential role for these molecules in the development and maturation of wine grapes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".