Distribution of Bark Beetle Attacks After Whitebark Pine Restoration Treatments: A Case Study
Bibliographic record
Abstract
Abstract Whitebark pine (Pinus albicaulis Engelm.), an important component of high elevation ecosystems in the western United States and Canada, is declining due to fire exclusion, white pine blister rust (Cronartium ribicola J.C. Fisch.), and mountain pine beetle (Dendroctonus ponderosae Hopkins). This study was conducted to evaluate the effects of whitebark pine restoration treatments on the distribution of bark beetle attacks. At a site in Idaho, silvicultural treatments were implemented in summer 1998 and 1999, with prescribed burning implemented in Oct. 1999. Permanent plots (400m2) were established during summer 1999 within each treatment and monitored for 4 years. Within plots, tree characteristics were measured and a bark beetle survey was conducted. Bark beetle attacks remained low throughout the study; however, there was an increase in bark beetle attacks in 2000 after the prescribed burning. By years 3 and 4, there were virtually no successful attacks. Although bark beetles were not a serious concern at the site assessed in this study, our results indicate that managers should consider and monitor the bark beetle component of these ecosystems when implementing restoration treatments. If baseline bark beetle populations are high at the time of implementation, our results indicate that increases in beetle activity would be expected in some treatments, perhaps requiring mitigation. West. J. Appl. For. 20(2):110–116.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".