A worldwide perspective on the management and control of Dothistroma needle blight
Bibliographic record
Abstract
Summary Dothistroma needle blight (DNB) caused by Dothistroma septosporum and Dothistroma pini is a damaging disease of pine in many countries. The disease led to the abandonment of planting susceptible Pinus species in parts of Africa, Asia, Australasia, Europe and North America. Although the disease can be effectively controlled using copper fungicides, this chemical is only routinely applied in forests in New Zealand and Australia. Other management tactics aimed at making conditions less favourable for disease development, such as thinning or pruning, may be effective on some, but not all, sites. Disease avoidance, by planting non‐susceptible species, is the most common form of management in Europe, along with deployment of hosts with strong disease resistance. Although D. septosporum is present almost everywhere Pinus is grown, it is important that an effort is maintained to exclude introductions of new haplotypes that could increase virulence or enable host resistance to be overcome. A global strategy to exclude new introductions of Dothistroma and other damaging forest pathogens, facilitated by collaborative programmes and legislation, is needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.010 | 0.005 |
| Insufficient payload (model declined to judge) | 0.028 | 0.004 |
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".