Juice attributes of Ontario-grown culinary (dessert) apples for cider
Bibliographic record
Abstract
Continued interest in the commercial production of cider in Ontario has revealed that there is a lack of information available to apple producers and cidermakers on the juice attributes of culinary dessert apple for making cider and how they may vary from orchard to orchard. A two-year study was conducted to characterize the juice characteristics of 18 common culinary dessert apple cultivars grown at several locations in Ontario. Juice was assayed for soluble solids concentration, pH, titratable acidity (TA), juice extraction efficiency, yeast assimilable nitrogen (YAN), polyphenols, and soluble solids to TA ratio. Significant differences among cultivars were observed for all parameters, which varied by orchard and year, except juice extraction efficiency. These data underly the importance of assaying juice every growing season prior to fermentation. Cidermakers should focus on juice characteristics from culinary apples that are more difficult to alter (with amendments), and use juice with lower pH, higher TA and polyphenols, and moderate to higher YAN to optimize cider quality and flavour. Overall, this study will inform cidermakers of which culinary apple cultivars might be preferable for fermentation as well as how they might be blended to optimize cider quality. Notwithstanding annual and orchard variation, cultivars that meet these criteria include Cortland, Crimson Crisp, Honeycrisp, Jonagold, McIntosh, and Northern Spy. If making cider from Ambrosia, Crispin, Empire, Fuji, Gala, Golden Delicious, or Idared, cidermakers should expect to blend the juice from these cultivars to create a more fully balanced juice prior to fermentation.
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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 | 0.000 | 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 | 0.000 | 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) | 0.001 | 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 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".