A review of Pinaceae resistance mechanisms against needle and shoot pathogens with a focus on the <i>Dothistroma</i>–<i>Pinus</i> interaction
Bibliographic record
Abstract
Summary Dothistroma needle blight (DNB), caused by Dothistroma septosporum and Dothistroma pini, is a highly damaging disease of pine. DNB was originally considered a problem on exotic Pinus radiata plantations in the Southern Hemisphere and on both exotic and native pines in parts of North America in the 1960s. Since the mid‐1990s, however, DNB has increased in importance in various parts of the world, including Europe. On susceptible species, DNB causes premature needle drop, a loss of yield and, in some circumstances, mortality. In some areas, DNB is controlled by the application of copper‐based fungicides and silvicultural techniques, such as thinning and pruning. In New Zealand, there has also been a long history of selection of more resistant P. radiata for use in breeding programmes. A richer understanding of the resistance mechanisms involved in the Dothistroma–Pinus interaction will play a critical role in helping the development of sustainable integrated DNB management strategies. This review therefore summarizes current knowledge of defence mechanisms involved in the defence of Pinaceae against needle and shoot pathogens and identifies research gaps. Collaborative research efforts from countries directly or indirectly affected by DNB are rapidly generating new knowledge to address these gaps.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".