Cold hardiness of select apple cider cultivars in Canada
Bibliographic record
Abstract
There is increasing interest in growing European origin apple cultivars for the production of hard cider in Canada; however, little is known about their winter hardiness. Eleven promising cider cultivars were evaluated for cold hardiness over two consecutive winters and compared with the winter tender cultivar ‘Golden Delicious’. Sections of the current season’s dormant shoots were frozen in a series of test temperatures ranging from −20 °C to −40 °C in a programmable freezer. Xylem tissue browning ratings were used to assess injury after thawing. The temperature of incipient damage (TID), the warmest temperature at which 1-yr-old shoot segments begin to show injury, was obtained from tissue browning curves using non-linear regression. TID varied significantly among cultivars and between sampling years. Overall, the cultivars could be classified according to relative winter hardiness as follows: Ashmead’s Kernel, Bramley’s Seedling (very tender) < Calville Blanc d’Hiver, Porter’s Perfection, Bulmer’s Norman (intermediate) < Crimson Crisp, GoldRush, Golden Delicious, Enterprise, Yarlington Mill, Enterprise (hardy) < Golden Russet (hardy). These data indicate nearly a 10 °C range in winter hardiness amongst the 11 cultivars studied, depending on the sampling date. Ashmead’s Kernel and Bramley’s Seedling appear to be particularly winter tender, whereas Bulmer’s Norman, Porter’s Perfection, and Calville Blanc d’Hiver demonstrated less hardiness during three of the four sampling dates. Based upon these findings, it would be prudent to consult long-term climate normals and consider the frequency of extreme weather events for potential susceptibility to winter injury, particularly prior to establishing more injury-prone cultivars.
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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".