Dothistroma needle blight, weather and possible climatic triggers for the disease's recent emergence
Bibliographic record
Abstract
Summary Dothistroma needle blight (DNB), caused by the two fungiDothistroma septosporumandD. pini,is a major disease of pines with a worldwide distribution. Increases in the incidence and severity of disease in areas where the disease has long been established and notable range expansions have both recently been observed. The aim of this review was to assess the relationship betweenDNB, weather factors and climate to better understand possible underlying causes of this recent intensification in disease. A substantial body of literature shows that the life cycles of the fungi are closely related to weather factors such as precipitation and temperature. Given the rapid response ofDNBto favourable weather conditions, it seems plausible that changes in disease behaviour could be due to changes in climate. The recurrent El Niño‐Southern oscillation (ENSO) phenomenon influences patterns of temperature and precipitation in many regions of the world, often resulting in warmer and wetter conditions than normal. We found that since the 1950s, four of the past five strong El Niño events appear to have coincided with reports of increasedDNBactivity on an intercontinental scale. The lack of long‐term standardized data records limits our ability to fully interpret this relationship, but the projected future climatic conditions in the Northern Hemisphere appear to be increasingly favourable for the disease. Still, other areas of the world may become less favourable, and further research is required to be able to accurately predictDNBoutbreaks and their impact on pine forests in the future.
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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 | 0.000 | 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".