Predicting effects of climate change on Swiss needle cast disease severity in Pacific Northwest forests
Bibliographic record
Abstract
Swiss needle cast of Douglas-fir (Pseudotsuga menziesii) is caused by the ascomycete Phaeocryptopus gaeumannii. Symptoms are foliage chlorosis and premature needle abscission due to occlusion of stomata by the ascocarps of the pathogen, resulting in impaired needle gas exchange. Growth losses of 20%-50% due to Swiss needle cast have been reported for approximately 150 000 ha of Douglas-fir plantations in western Oregon since 1996. In the western Coast Range of Oregon and in New Zealand, winter temperature is strongly correlated with pathogen abundance. Models for predicting disease severity based on winter temperature account for 77% and 78% of the variation in 1- and 2-year-old needles, respectively, for western Oregon sites and approximately 80% for New Zealand. A trend of temperatures increasing by 0.2–0.4 °C during the winter months and spring precipitation increasing by 0.7–1.5 cm/decade since 1970 suggests that regional climate trends are influencing the current distribution and severity of Swiss needle cast disease. Continuing winter temperature increases predicted for the Pacific Northwest of approximately 0.4 °C/decade through 2050 suggest that the severity and distribution of Swiss needle cast is likely to increase in the coming decades as a result of climate change, with significant consequences for Pacific Northwest forests.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".