Climate change impacts and the reshaping of Canadian viticulture
Bibliographic record
Abstract
Shifting climate patterns across wine-growing areas of Ontario, British Columbia, Nova Scotia, and Quebec are driving the development of new viticultural potential within established Canadian wine regions. Changing trends of critical climatic variables and indices, such as near-surface temperature (NST) and growing degree days, indicate that growing conditions are changing. This research assesses NST and seasonal precipitation trends from 1994 to 2100 for Canadian viticulture, focusing on the primary established growing regions. Using multi-model CMIP6 spatial-temporal averages from the NEX-GDDP-CMIP6 dataset available on Google Earth Engine, this research aims to understand future NST and seasonal precipitation trends with climate scenarios SSP245 and SSP585 and discuss possible effects on viticulture on a near-term (2015-2050) and long-term (2050-2100) basis. Minimum, average, and maximum NST trends demonstrated statistically significant increases across all regions, with similar increasing precipitation trends across the growing season. Increasing trends, especially trends of extreme temperature, can all influence grape quality and, ultimately, wine quality. Outcomes suggest warmer growing climates, which may benefit wine producers, but the increasing frequency of extreme climate-change-related events such as drought, heatwaves, or extreme rainfall suggests potential future challenges that will require careful management.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".