Impact of crop level and harvest date on anthocyanins and phenolics of red wines from Ontario
Bibliographic record
Abstract
Cabernet Sauvignon (CS) and Cabernet franc (CF) vines were subjected to two crop levels (full, half) and three harvest dates (earliest to latest; T0, T1, T2) over two vintages. Wines were analyzed for anthocyanins, phenolics, and proanthocyanidins. Crop level increased CS hue (2011–2012), increased CS pH and reduced CS color intensity (2012), and reduced CF hue (2012). Harvest date had a greater effect than crop level, with many treatment interactions. Half crop (2011) increased three CS anthocyanins plus procyanidin B. Extended harvest increased eight compounds. Quercetin and (+)-catechin decreased in T1. Crop reduction (2012) increased malvidin-3-coumarylglucoside and (+)-catechin, but decreased petunidin and delphinidin-3-coumarylglucoside. Harvest date (2012) impacted all but two compounds, with the highest anthocyanin concentrations in T1 wines. Gallic acid, (+)-catechin and resveratrol increased with harvest date, while three phenols decreased. Half crop (2011) increased CF peonidin. Extended harvest increased four phenols while three others decreased. Crop reduction (2012) increased delphinidin-3-acetylglucoside, cyanidin-3-coumarylglucoside and caffeic acid; (−)-epicatechin and p-coumaric acid decreased. Several anthocyanins and phenols decreased between T0 and T2, nine of 13 anthocyanins decreased between T0 and T1, while others decreased from T1 to T2. Gallic acid and (+)-catechin increased with harvest date; (−)-epicatechin, p-coumaric acid, quercetin, and resveratrol decreased.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".