Determining adaptive capacity to climate change in the grape and wine industry
Bibliographic record
Abstract
The agricultural sector is sensitive to climate change (CC) and associated extreme weather events, but suffers in Canada from little strategic research and policy on CC adaptation. The wine industry is often considered the 'canary in the coal mine’ for CC due to the narrow geographic and climatic range required by many grape varieties. Adapting to current and projected CC challenges requires industry stakeholders to determine the risks and benefits of CC and develop a level of adaptive capacity. The objectives of this study were to develop a metric for assessing the adaptive capacity of a grape/wine industry, and apply that tool to the Ontario case. A framework was developed and represented as a three-tiered, hierarchical structure, which included eight operational and strategic determinants (financial, institutional, technological, political, knowledge, perception, social capital, and diversity), and 28 specific indicators. A comprehensive questionnaire was created from this framework consisting of 26 statements to which participants indicated their level of agreement. 42 Ontario wine industry members completed the questionnaire via an on-line survey. Results show that the Ontario wine industry has some adaptive capacity in all the key resources assessed. Perception, diversity and knowledge are the determinants with the greatest capacity, while political and technological are the most limited. Overall, industry stakeholders do not perceive they are at a coping threshold and are interested in learning how to better adapt to the impacts of CC. Results are discussed in the context of opportunities to enhance adaptive capacity in the grape/wine community.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".