Blister rust and western forest biodiversity: ecology, values and outlook for white pines
Bibliographic record
Abstract
Summary Eight white pine species are widely distributed among the forests of western Canada and the United States. The different forest communities with these species contribute biodiversity to the western landscape. The trees themselves provide various ecosystem services, including wildlife habitat and watershed protection. White pine communities range in elevation from lower to upper treeline, in successional stage from seral to climax, and in stand type from krummholz to closed‐canopy forest. Many white pine species are moderately to strongly fire‐dependent for regeneration; several species are extreme stress tolerators and persistent on harsh sites. Among the white pines are the oldest‐living trees, the world’s largest pines, species dependent on birds for seed dispersal, species important for grizzly bear habitat and species of high commercial timber value. The principal threats to white pine populations are blister rust (Cronartium ribicola, pathogen), fire suppression, succession, mountain pine beetle and climate change. Severe population declines in several white pine species are attributed to losses caused by these factors acting either alone or together, and sometimes in concert with logging and other land‐use changes. The importance and particular interactions of these threats vary by region and species. For example, many northern and western populations of whitebark pine are seriously declining from a combination of mountain pine beetle outbreaks and severe blister rust infestations. As whitebark pines provide many keystone services on high‐elevation sites, their loss would impact forest composition and structure, succession, biodiversity, and ecosystem services. Although there are serious challenges to science‐based management and conservation (especially in remote American wilderness areas), prompt and effective intervention promoting regeneration of blister rust‐resistant white pines could mitigate these severe impacts.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".