Evaluation of selected cider apple (<i>Malus domestica</i> Borkh.) cultivars grown in Ontario. I. Horticultural attributes
Bibliographic record
Abstract
Twenty-eight apple cultivars were selected for their potential for hard cider production in Ontario. An experiment was conducted to evaluate their horticultural potential in the province. After being planted in spring 2015, the trees were evaluated annually for their survival, tree height and spread, trunk growth, flowering dates, flower counts, fruit per tree, pre-harvest drop, crop load, fruit weight, fruit firmness, juicing extraction efficiency, and harvest dates. These horticultural attributes were sufficient to discriminate between cultivars. Additional exploratory analyses indicated a relationship between horticultural attributes and a cultivar’s origin, with British cider cultivars blooming the latest, American cider apples producing the most juice, and French cider cultivars having the highest pre-harvest fruit drop. Cultivars in this study that show promise for continued research in Ontario include Binet Rouge, Bramley’s Seedling, Breakwell, Bulmer’s 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.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".