Effect of Wine pH and Bottle Closure on Tannins
Bibliographic record
Abstract
The impact of wine pH and closure type on color, tannin concentration, and composition was investigated. A single vintage of Cabernet Sauvignon wine was divided into three batches, the pH was adjusted to 3.2, 3.5 or 3.8, and the wines were bottled under screw caps with either SaranTin (ST) or Saranex (Sx) liners. After 24 months, the tannin concentration, tannin percent yield (relating to the proportion of acid-labile interflavan bonds), and the mean degree of polymerization (mDp) had decreased significantly, all of which can contribute to the softening of wine astringency with aging. The higher pH wines contained less percent (-)-epicatechin 3-O-gallate subunits, whereas the Sx pH 3.2 wines were significantly lower in percent yield and mDp than the other wines. Overall, the tannin structure and wine color of the lower pH wines (pH 3.2) bottled under Sx screw caps changed more rapidly with aging than those of the higher pH wines (pH 3.8) bottled under ST screw caps.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".