The potential impact of climate change on the occurrence of winter freeze events in six fruit crops grown in the Okanagan Valley
Bibliographic record
Abstract
The main limitation to fruit production in the Okanagan Valley is winter injury. Examination of historical records between 1916 and 2006 revealed 16 severe winter-kill events with two occurring in November, eight in December, four in January and two in February. Extreme low minimum temperatures are associated with poor production of grape, peach, apricot, sweet cherry, pear, and apple, and although all are subject to winter injury during most of the 4-mo period, the time when each crop is most at risk differs. Grapes, apples and sweet cherries are more subject to injury in the early stages of acclimation during November to mid-January, whereas pears, peach and apricot are more subject during January and February. During the period 1948-2006, Arctic outflows were associated with all of these winter freeze events. This synoptic weather pattern was an infrequent event but had a great impact on production. A decrease in frequency and increase in minimum temperature of Arctic outflows appeared to be associated with the warming trends of the region during winter and early spring, although a slight increase in frequency of Arctic outflows was observed during late autumn. If this pattern in climate change continues, an extension of the northern range of the grapes, apples and sweet cherries in this region might not be as great as anticipated, whereas the production of pears, peaches and apricots might be expanded. Key words: Climate change, fruit, Okanagan Valley, winter injury, Arctic airflow
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".