Horticultural and juice attributes of cider apple (Malus domestica Borkh.) cultivars grown in Ontario, the endogenous development of yeast assimilable nitrogen in apple juice, and the effects of exogenous nitrogen supplementation on the fermentation of apple juice
Bibliographic record
Abstract
Apple growers in the cider industry would like to grow regionally suitable cultivars and optimize desirable juice attributes for cider. Regional conditions and orchard practices affect juice composition, including sugar, acidity, polyphenols, and yeast assimilable nitrogen (YAN). The main objectives of this research were to identify which cultivars are best suited for growth in Ontario and to determine the effect of orcharding practices on the juice YAN composition and fermentation. Horticultural and juice data were collected on 28 cultivars from initial planting in 2015 through the 2018 harvest. Cultivars that show promise for continued research in Ontario include ‘Binet Rouge’, ‘Bramley’s Seedling’, ‘Breakwell’, ‘Bulmers Norman’, ‘Calville Blanc d’Hiver’, ‘Cline Russet’, ‘Cox Orange Pippin’, ‘Crimson Crisp®’, ‘Dabinett’, ‘Enterprise’, ‘Esopus Spitzenberg’, ‘Golden Russet’, ‘GoldRush’, ‘Medaille d’Or’, ‘Porter’s Perfection’, and ‘Stoke Red’. In another experiment, foliar urea spray was applied to ‘Crimson Crisp®’ trees to determine its effect on YAN concentrations. It was determined that YAN concentrations in ‘Crimson Crisp®’ apple juice are stable for the month before harvest and after storage and that high levels of fertilization will lead to an increase in aspartic acids and asparagine. Fermentations of ‘GoldRush’ juice with varying diammonium phosphate and sucrose supplementation were performed to investigate YAN requirements for cider production, which showed that YAN and sugar are associated in cider fermentations and that there is an optimal proportion between the two for a fast and complete fermentation. This research will help growers select orchard and cider production practices that will result in higher quality cider.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".