Current health issues and management strategies for white pines in the western United States and Canada
Bibliographic record
Abstract
Summary The introduced pathogenCronartium ribicola, cause of white pine blister rust, has spread across much of western North America and established known infestations within all but one species of white pine endemic to western Canada and the United States. Blister rust damage to severely diseased trees reduces reproduction and survival. Severe losses in white pine populations have resulted in site conversions to other species and seriously impacted resource values for timber, wildlife, watershed, recreation, aesthetic and other ecosystem services. In addition to blister rust, other major forest health threats and challenges to sustaining or restoring white pine populations are infestations of other pathogens, insects, fire, management practices that favour other tree species, and climatic change. Recent, large‐scale outbreaks of mountain pine beetle have raised concerns for the viability of some white pine populations. In the 1960s, forest disease management for western white pine and sugar pine shifted fromRibeseradication to planting seedlings selected for better survival and resistance to blister rust. Seed orchards for producing improved white pines have been established, but deployment of that improved stock is hampered by a lack of planting opportunities. The inheritance and mechanisms of resistance are best known for western white pine and sugar pine; but new work is extending an understanding of genetics to all the western species of white pine. Current management efforts are focused on locating and protecting individual trees resistant to blister rust and assessing their disease resistance and other adaptive traits. In response to the threats from blister rust, the strategic goal is to sustain or restore viable white pine populations in western forest ecosystems. The four action components of the strategy are: (1) conserve genetic resistance toC. ribicola; (2) reduce the risk of adverse impact in stands currently uninfested; (3) restore and maintain white pines where blister rust is causing impacts and (4) assess and monitor the health and management of white pines. Successful implementation requires long‐term support for coordinated efforts of management and research agencies, forest industry and an informed public.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".