Are Canada’s best wines from Ontario or British Columbia? An analysis of the All Canadian Wine Championships results, 2010–2022
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study demonstrates that time-series data from national wine competitions can be used to extract valuable information on a country’s wine industry, including wine styles, most successful varieties, and regional performance. Using annual data from the All Canadian Wine Championships from 2010 to 2022, covering 16,140 wines, the study also answers the perennial Canadian question of whether the country’s best white and red wines come from Ontario or British Columbia. The wines were compared using four indicators: trophies and double gold medals; average score per entry; weighted award scores; and value for money. On all four indicators, overall British Columbia slightly outperforms Ontario almost every year. British Columbia is therefore Canada’s leading province in terms of quality and value for money; moreover its quality edge over Ontario has increased in recent years.
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.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it