A global climatic risk assessment of pitch canker disease
Bibliographic record
Abstract
Pitch canker is a devastating disease of Pinus spp. and Pseudotsuga menziesii (Mirb.) Franco. The pathogen responsible for this disease, Fusarium circinatum Nirenberg & O'Donnell, has spread to many countries within the last three decades. The susceptibility of the widely planted commercial species Pinus radiata D.Don to this pathogen has been of concern to pine forest industries worldwide. Using the process-based distribution program CLIMEX, the global risk of pitch canker establishment was predicted based on a number of climatic variables. The predicted risk of pitch canker establishment by CLIMEX fit well with regions known to have the disease, such as the southeastern United States and Spain. Conversely, the model predicted that the climate in California was not optimal for pitch canker, which fits with the observed lower frequency of natural infections and the strong association with insects in this region. Likewise, Chile, which is known to have F. circinatum in the nurseries but not in the plantation forests, was also predicted to have marginal to suitable climatic conditions for pitch canker establishment. Regions of China, Brazil, Australia, and New Zealand were predicted to have optimal climate conditions for disease establishment. Thus, continued strict quarantine regulations are recommended to prevent the establishment and spread of this pathogen in these countries.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".