Climate Change Impacts on Viticulture in Canada
Bibliographic record
Abstract
Canada's viticultural growing climates are changing and redefining the potential of the Canadian wine industry. Growing seasons are evolving, as observed through the changing trends of important variables, such as near-surface temperature and seasonal precipitation. Using open access NEX-GDDP-CMIP6 data available in Google Earth Engine, this research investigated the trend evolution of key viticultural variables, near-surface temperature (minimum, average, maximum) and seasonal precipitation, across various temporal timespans within the primary Canadian wine-producing provinces of Ontario, British Columbia, Quebec and Nova Scotia between 1994-2100. In addition, two shared socioeconomic pathways (SSPs), SSP245 and SSP585, were used to help build an understanding of how the key viticultural variables of interest may change in the near-term (2015-2050) and the long-term (2051-2100). Statistically significant near-surface temperature increases were demonstrated across all wine-growing provinces alongside seasonal precipitation increases over the growing season. Temperature increases can have an impact on the quality of wine produced as well as the type of grape variety used, which could be beneficial to Canadian wine producers. The Canadian wine industry is typically dominated by grape varieties reflective of cooler growing climates. Increasing temperatures, especially over the growing season, may allow for the utilization of grape varieties found in other wine-growing areas with warmer climates, like southern Europe. However, the increasing frequency of extreme events, like rainstorms, droughts, and heat waves, will present barriers to the potential growth of the Canadian wine industry.
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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 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".