Assessing the influence of climate controls on grapevine biophysical responses: a review of Ontario viticulture in a changing climate
Bibliographic record
Abstract
Climate change presents unique challenges for grape growers across the world. In Ontario, three distinct viticultural regions are experiencing climatic shifts towards warmer growing seasons. According to historical records collected from Environment and Climate Change Canada, Lake Erie North Shore has transitioned from an intermediate-to-warm growing season classification, the Niagara Peninsula from the lower to upper limits of the intermediate zone, and Prince Edward County from cool to intermediate, when analyzing their average growing season temperatures. Terroir is directly related to vine water status, an indicator of grapevine stress. Biophysical responses controlled by air temperature and precipitation include fluctuations in vapour pressure deficits, evapotranspiration, and water-use-efficiency rates, as well as soil water content levels. By conducting an extensive literature review, the development of a conceptual model addresses how variations in climatic controls, under the scope of climate change, may influence grapevine water status, biophysical responses, and associated production outcomes for Ontario vineyards. Cool-to-intermediate air temperatures, when paired with increased precipitation will lead to no or low vine stress, increasing photosynthesis and transpiration rates, and variable plant water-use-efficiency levels, producing higher yields and lower quality grapes, if no management strategies are applied. Oppositely, higher air temperature as a product of climate change, when paired with variable precipitation may produce mild-to-severe stress, reducing yield, and increasing grape quality. With the appropriate management strategies, both traditional and new, growers may be able to accommodate for the influence of climate change on their vineyards.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".