Climate change adaptation in the Canadian wine industry: Strategies and drivers
Bibliographic record
Abstract
The wine industry is and will continue to be impacted by climate change. The adaptation of vineyards and winery practices is therefore paramount to the success of winegrowing operations around the globe. We surveyed winegrowers across Canada to assess their adaptation status, the strategies they currently use or plan to implement to cope with the effects of climate change, and the drivers that influence the adoption of adaptation measures. We found that Canadian winegrowers are most adapted to weather events associated with precipitation and drought and less adapted to other extreme weather events. Our results also show that winegrowers' concern about climate change exerts a small, but significant, positive effect on both climate change adaptation and the willingness to adapt in the future. Moreover, winegrowers with smaller operations are less likely to be adapted to some weather events associated with climate change. This research provides an overview of the state of climate change adaptation by winegrowers in Canada and supports the implementation of context‐specific adaptations in wine regions throughout the country.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".