Comparison of Extraction Protocols To Determine Differences in Wine-Extractable Tannin and Anthocyanin in <i>Vitis vinifera</i> L. cv. Shiraz and Cabernet Sauvignon Grapes
Bibliographic record
Abstract
Cabernet Sauvignon and Shiraz grapes were sourced from different regions within Australia, and microvinified with a skin contact period of 6 days. Grape samples were extracted using two protocols: a 15% v/v ethanol, 10 g/L tartaric acid extract of gently crushed berries (wine-like, WL) and a 50% v/v ethanol, pH 2 extract of grape berry homogenate. It was found that in WL extracts, grape tannin and anthocyanin concentrations were strongly related to wine tannin, anthocyanin and color density achieved during the skin contact period. No relationship was observed for grape tannin concentration analyzed in homogenate extracts and wine tannin, but a strong, positive relationship was found for anthocyanin concentration. When the data obtained from homogenate extraction was treated separately by grape variety, a stronger relationship between grape and wine tannin concentration was observed. Tannin compositional analysis in wines indicated that higher tannin concentrations were due to the extraction of tannin of higher molecular mass during fermentation, most likely from grape skins.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".