Hard Pressed to Find A Difference: Evaluating the total tannin content of Cabernet franc L cv. wines, made using pre- and post-fermentation pressing treatments
Bibliographic record
Abstract
Total tannin concentrations were monitored in Cabernet franc grapes in 2018 and 2019 from two vineyard areas in the Niagara Peninsula, Ontario. Total tannins were measured using the methyl cellulose precipitation (MCP) assay. In 2018, post-fermentation pressing treatments of 100kPA, 150kPA and 200kPA were applied with a control (no press treatment). In 2019, pre-fermentation pressing treatments in combination with juice removal (saignée) were applied with a control (no pre-fermentation treatment). Free sorting and consumer preference testing of the 2018 wines were evaluated using trained panellists and a consumer panel. Pre- and post-fermentation press treatments had little impact on the tannin concentrations in wines post fermentation. Observed trends over time suggest treatments have an influence in the behavior of tannin polymerization and stability, but these results are specific to vineyard site. This suggests that Cabernet franc varietal wines can be produced using low press-treatments without compromising the concentration of total tannins which may contribute to improved wine quality.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | medium |
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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".