Bibliographic record
Abstract
Canada was not well known with its wine industry before the 1990s. Neither was so the wine industry of the Niagara region of Canada. However, Niagara has now emerged as one of the well-known wine regions of the world. Even if the region produced small amount of wines, it is almost the only wine of the world where a particular type of wine, i.e., the Icewine is produced stablly over the last a few decades. Based on the seven winter climate, the Niagara region has established a major wine cluster, consisted of wineries, wine tour, festivals and supporting activities from research, educational, and governmental institutions. This paper introduces how the region has created the wine cluster in a short period and analyses factors contributing to and the mechanisms of the cluster. It identifies factors contributing to the success as entrepreneurship of the founders of the core wineries, such as Cave Spring. Hillebrand, and Reif, strong governmental policies, and supports from research and educational institutions, such as Brock University and Vineland Research and Innovation Centre.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.015 |
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".