Regional outbreak dynamics of <i>Dothistroma</i> needle blight linked to weather patterns in British Columbia, Canada
Bibliographic record
Abstract
Dothistroma needle blight (DNB) has caused extensive mortality in plantations of lodgepole pine in northwestern British Columbia, Canada, and even mature pine trees in natural forests are succumbing. We examined a number of explanatory models to determine which temperature or precipitation variable is most important in explaining DNB outbreak occurrence. We compared a multicentury tree-ring outbreak reconstruction with mean monthly temperature and total monthly precipitation during the individual and seasonal spring (April, May, June) and summer (July, August, September) months. A trend towards increased August minimum temperatures appears to be an important climate factor contributing to the spread of the disease. Graphical comparisons of total April precipitation reveal that periods of above-average precipitation coincided with periods of outbreak increase. Decreases in August minimum temperature also correspond to decreases in outbreak severity. Even more pronounced was a spike in spring precipitation and its association with the current epidemic. The correspondence of past outbreaks with periods of wet and warm conditions suggests that regional climate trends drive DNB behavior. The extent and severity of the current disease epidemic raises the possibility that the key factors that drive outbreaks have become more frequent, enabling the emergence of DNB as a serious disease.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".