Preserving Agriculture Through Wine: \nExamining The Opportunity For Ontario’s Wine Industry To Pioneer Agricultural Resilience In The Face Of Climate Change.
Bibliographic record
Abstract
The wine industry is a globally established example of an elite agricultural and consumer business that is socially and economically important to sustain. Climate change is already negatively impacting the industry and is predicted to become even more unstable in the future. Vintners are uniquely primed for futures-thinking, and an opportunity thus exists for the wine industry to pave the way for sustainable climate leadership. Using Curry and Hodgson’s Three Horizons model as an analytical framework, this project aims to explore an opportunity for the Ontario wine industry to become a leader in climate crisis resilience. First, context is set through a better understanding of challenges that live at the intersection of climate change, agriculture and the wine industry. Next, insights are drawn from examining both the current and imagined future state of Ontario’s wine industry. And finally, the research closes with a discussion of proposed strategies that can possibly bring a preferred Ontario wine industry future to life.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.002 |
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".