Bibliographic record
Abstract
Th e paper presents a variety trial and technological evaluation of the wine material from Frontenac Blanc grape, isolated as a color mutation in Canada plantations of the Frontenac Gris variety in 2006. Th e study was carried out in 2020–2022 in compliance with vitiviniculture recognized methodology in soil and climatic conditions of Potapenko All-Russian Research Institute of Viticulture and Winemaking, a branch of Federal Rostov Agrarian Scientifi c Centre, Novocherkassk, Rostov Oblast, Russia. Vineyards are open-earth, rainfed, graft ed, with Kober 5BB rootstock, 3.0×1.5 m planting scheme, shaping: double-shouldered low (40 cm) horizontal cordon, short pruning on knots. Agricultural technology is standard, generally recognized. Frontenac Blanc is found to obtain high winter hardiness, promising for open-earth cultivation. Th e variety has good resistance to infections: to mildew (2.0 score), oidium (2.5 score); grey rot has not been observed. Th erefore, frequent use of pesticides is not required and vineyard maintenance is simplifi ed. Th e high level of sugars (248 g/dm3) and t itratable acids (10.4 g/dm3) in t h e juice ensures a harmonious wine with well-balanced acidity. Due to such characteristics, the variety is considered to be promising for the production of diff erent wine styles, including dry still, semi-dry, as well as sparkling and liqueur wines. Th e wine material from Frontenac Blanc presents a distinguishing character, bright fruit aromas, rich structured fl avor, and lingering fi nish. Th e average tasting score of the wine material comprises 8.6 points (on a 1–10 scale). Th e variety has a high yield (16 tonnes/ha), which does not adversely aff ect the quality of products. Frontenac Blanc is resistant to cold and early ripening period (120 days) and can be eff ectively grown in regions with previously limited production due to extreme climatic conditions. Considering that, an innovative development of the wine industry in areas with cool and uncertain climate possesses a high potential.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".