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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.009 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| 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; both teacher heads agree on what is shown here.
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".