Subwatershed-Level Lodgepole Pine Attributes Associated with a Mountain Pine Beetle Outbreak
Bibliographic record
Abstract
Mountain pine beetle (Dendroctonus ponderosae Hopkins; MPB) is an aggressive bark beetle that attacks numerous Pinus spp. and causes extensive mortality in lodgepole pine (Pinus contorta Douglas ex Loudon; LPP) forests in the western United States and Canada. We used pre-outbreak LPP attributes, cumulative MPB attack severity, and areal extent of mortality data to identify subwatershed-scale forest attributes associated with severe MPB-caused tree mortality that occurred across the Northern Rockies, USA from 1999–2014. We upscaled stand-level data to the subwatershed scale to allow identification of large LPP areas vulnerable to MPB. The highest mortality occurred in subwatersheds where LPP mean basal area was greater than 11.5 m2 ha−1 and LPP quadratic mean diameter was greater than or equal to 18 cm. A coarse assessment of federally-owned LPP-dominated forestland in the analysis area indicated about 42% could potentially be silviculturally treated. Silvicultural management may be a suitable option for many LPP forests, and our hazard model can be used to identify subwatersheds with LPP attributes associated with high susceptibility to MPB across landscape spatial scales. Identifying highly susceptible subwatersheds can help prioritize general areas for potential treatments, especially where spatially extensive areas of contiguous, highly susceptible LPP occur.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".