Projected changes in risks of winter damage to fruit trees and plant hardiness zones in Canada
Bibliographic record
Abstract
Winter climate conditions, especially extremely low temperatures, constrain the production of fruit trees and other perennial crop species in Canada. Significant decreases in cold extremes under climate change may result in changes in plant hardiness zones and the distribution of crop species across the country. Climate warming might also bring changes to climate conditions that affect fall hardening, loss of cold hardiness due to winter thaws, and spring frost damages. Using the most up-to-date climate projections, we provide projected changes in the risks of damages to fruit trees during winter based on five agroclimatic indices and changes to the United States Department of Agriculture (USDA) plant hardiness zones based on long-term averages of annual extreme minimum air temperatures in the near-term (2030s, 2020–2049), mid-term (2050s, 2040–2069), and distant future (2070s, 2060–2089). Our results suggest that climate change might be beneficial for fruit trees across Canada with (1) improved fall hardening because of more time to acquire cold hardiness due to delayed first fall frost and (2) decreased winter coldness with increases in annual minimal temperatures and decreases in the accumulation of cold degree-days below –15 °C. The risks of loss of cold hardiness due to winter thaws will increase slightly while the risks of spring frost damages to buds will be largely unchanged. Under a warmer distant future, the USDA plant hardiness zones across Canada would increase by 1.5–2 “full” zones, which may lead to the introduction of new fruit tree species and opportunities for Canadian producers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".