Blacks Mountain Experimental Forest: bark beetle responses to differences in forest structure and the application of prescribed fire in interior ponderosa pineThis article is one of a selection of papers from the Special Forum on Ecological Studies in Interior Ponderosa Pine — First Findings from Blacks Mountain Interdisciplinary Research.
Bibliographic record
Abstract
Mechanical thinning and the application of prescribed fire are commonly used tools in the restoration of fire-adapted forest ecosystems. However, few studies have explored their effects on subsequent amounts of bark beetle caused tree mortality in interior ponderosa pine, Pinus ponderosa Dougl. ex P. & C. Laws. var. ponderosa. In this study, we examined bark beetle responses to creation of midseral (low diversity) and late-seral stages (high diversity) and the application of prescribed fire on 12 experimental units ranging in size from 76 to 136 ha. A total of 9500 (5.0% of all trees) Pinus and Abies trees died 2 years after treatment of which 28.8% (2733 trees) was attributed to bark beetle colonization. No significant difference in the mean percentage of trees colonized by bark beetles was found between low diversity and high diversity. The application of prescribed fire resulted in significant increases in bark beetle caused tree mortality (all species) and for western pine beetle, Dendroctonus brevicomis LeConte, mountain pine beetle, Dendroctonus ponderosae Hopkins, Ips spp., and fir engraver, Scolytus ventralis LeConte, individually. Approximately 85.6% (2339 trees) of all bark beetle caused tree mortality occurred on burned split plots. The implications of these and other results to sustainable forest management are discussed.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".