A Climate Change Impact Assessment (CCIA) of Key Indicators and Critical Thresholds for Viticulture and Oenology in the Fraser Valley, British Columbia, Canada
Bibliographic record
Abstract
Abstract Grape growth and wine production are both closely connected with weather and climate, making anthropogenic climate change a source of great uncertainty for the grape and wine industries. To assess the impacts of climate change on viticulture and oenology in the Fraser Valley, British Columbia, Canada, where no such assessment has been published to this date, a series of key indicators and critical thresholds were selected based on their relevance to the local climatology. Trends among these indicators and thresholds were calculated over a historic period (1970-2019) and projected over the 21st century for one intermediate-emissions and one high-emissions climate change scenario. Historic trends were assessed using Environment and Climate Change Canada weather station data from Abbotsford, British Columbia. Two statistical downscaling methods were evaluated based on their ability to reproduce observed conditions in the Fraser Valley and the most effective method was used to create projections of local, daily climate change scenarios. During the historic period, temperatures increased significantly, while precipitation and moisture variables displayed insignificant trends, reflecting the trends observed across other wine regions in Canada and the Northwestern United States. Throughout the 21st century, warming is expected to continue while precipitation decreases modestly. Extreme heat is projected to become far more frequent, while extreme cold and potential frost days become rare. In the short term, modifications to vineyard and winery operations may be sufficient adaptation strategies. Over the long term, new grape varieties will most likely need to be planted in existing vineyards and suitability for cool-climate varieties may shift northward in direction or upward in elevation.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".