Forest structure altered by mountain pine beetle outbreaks affects subsequent attack in a Wyoming lodgepole pine forest, USA
Bibliographic record
Abstract
Extensive outbreaks of the mountain pine beetle ( Dendroctonus ponderosae Hopkins) will alter the structure of many stands that will likely be attacked again before experiencing a stand-replacing fire. We examined a stand of lodgepole pine ( Pinus contorta var. latifolia Engelm. ex S. Watson) in Grand Teton National Park currently experiencing a moderate-level outbreak and previously attacked by mountain pine beetle in the 1960s. Consistent with published studies, tree diameter was the main predictor of beetle attack on a given tree, large trees were preferentially attacked, and tree vigor, age, and cone production were unimportant variables for beetle attack at epidemic levels. Small trees killed in the stand were killed based mainly on their proximity to large trees and were likely spatially aggregated with large trees as a result of the previous outbreak. We concluded that the driving factors of beetle attack and their spatial patterns are consistent across outbreak severities but that stand structure altered by the previous outbreak had implications for the current outbreaks in the same location. This study should catalyze additional research that examines how beetle-altered stand structure affects future outbreaks — an important priority for predicting their impacts under climate change scenarios that project increases in outbreak frequency and extent.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".