Bark beetles, fuels and future fire hazard in contrasting conifer forests of Greater Yellowstone
Bibliographic record
Abstract
The extent and severity of bark beetle (Curculionidae: Scolytinae) epidemics and the frequency of large, severe fires have reached unprecedented levels in recent decades, and these trends are expected to continue with ongoing climate change. Insects and fire have tremendous ecological and economic effects in western forests, yet their interactions are poorly understood. We combined field studies and simulation modeling to understand how bark beetle infestation and post-outbreak management affect fire hazard in two widespread but contrasting forest types, lodgepole pine (Pinus contorta) and Douglas-fir (Pseudotsuga menziesii) in the Greater Yellowstone Ecosystem (GYE) in northwestern Wyoming. We directly addressed key barkbeetle research priorities identified by US Forest Service scientists for the western US, via three primary questions and several supplemental studies. (1) How do effects of bark beetle outbreaks on fuel profiles and subsequent fire hazard differ between lodgepole pine and Douglas-fir forests? (2) How was the severity of recent fire in lodgepole pine and Douglas-fir forests affected by prior bark beetle infestation, and does the combination of beetle infestation and fire compromise forest recovery? (3) What post-beetle fuel treatments are likely to change the hazard of subsequent severe fire in lodgepole pine and Douglas-fir forests?
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".