Dothistroma needle blight and pitch canker: the current and future potential distribution of two important diseases of <i>Pinus</i> species
Bibliographic record
Abstract
Globally, pitch canker and Dothistroma needle blight are two of the most important diseases of pine species caused, respectively, by the pathogens Fusarium circinatum Nirenberg & O’Donnell and Dothistroma spp. ( Dothistroma septosporum (Dorog.) Morelet and Dothistroma pini Hulbary). The potential distributions of these two diseases under current global climate have previously been modelled and contrast strongly with each other. In this study, we used the process-based niche model CLIMEX to estimate the potential distribution of both diseases in the 2080s under six scenarios that include three contrasting global climate models, each with moderate and high CO2 emissions. For both diseases, under the future climate scenarios, there was a global reduction in the potentially suitable area. Among the three global climate models, this reduction ranged from 11% to 22% for Dothistroma needle blight and from 39% to 58% for pitch canker. The projected potential ranges of both diseases were significantly reduced for Africa, South America, and Australia. In Asia and North America, substantial reductions in potential area were generally projected for pitch canker, while little change to moderate levels of expansion were projected for Dothistroma needle blight. For Europe and New Zealand, expansion of suitable climate was projected under all climate change scenarios for both diseases.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".