Evaluation of selected cider apple (<i>Malus domestica</i> Borkh.) cultivars grown in Ontario. II. Juice attributes
Bibliographic record
Abstract
Twenty-eight apple cultivars were selected for their potential for hard cider production in Ontario; their juice characteristics were measured in 2017 and 2018, beginning two years after planting in 2015. After being harvested and pressed, each juice sample underwent analyses to determine soluble solids concentration (SSC), titratable acidity (TA), pH, yeast assimilable nitrogen (YAN), and polyphenolic concentration. Soluble solids concentration ranged from 10.6 °Brix in Brown’s Apple to 18.3 °Brix in Ashmead’s Kernel. Titratable acidity ranged from 31 as mg malic acid per 100 mL juice in Sweet Alford to 191 as mg malic acid per 100 mL juice in Bramley’s Seedling. The pH ranged from 2.88 in Breakwell to 4.76 in Sweet Alford. Yeast assimilable nitrogen concentration ranged from 60 mg YAN·L −1 juice in Medaille d’Or to 256 mg YAN·L −1 juice in Bulmer’s Norman. Polyphenols in juice ranged from 131 μg gallic acid equivalents (gae)·mL −1 juice in Tolman Sweet to 1042 μg gae·mL −1 juice in Stoke Red. Firmness ranged from 6.3 kg in Yarlington Mill to 11.7 kg in GoldRush. The relationships between these variables were also analyzed, showing a connection between acidity and juicing efficiency as well as a relationship between polyphenol concentration and fruit weight. Exploratory analyses indicated that juice attributes can be used to distinguish between cultivars and their origins. Cider producers can use these data to determine what to expect in juice from these cultivars.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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".