Environmental characteristics of mountain pine beetle infestation hot spots
Bibliographic record
Abstract
A combination of favourable temperatures and abundant host trees has resulted in a mountain pine beetle (Dendroctonus ponderosae Hopkins) epidemic over the majority of the lodgepole pine forests of British Columbia, Canada. Understanding temporal trends in the interactions between mountain pine beetle infestations and landscape characteristics can improve our understanding of beetle biology, inform modelling of future impacts, and support management. In this paper, we demonstrate a practical technique for characterizing spatial interactions between beetles and the environment. The locations with the highest-intensity infestations (hot spots) were identified using point data derived from annual helicopter-based surveys of beetle-infested pine, and a kernel density estimator. By examining the environmental characteristics associated with hot spots through time, an increased understanding of how the mountain pine beetle utilizes resources over large areas is generated. The effect of treatment on the persistence of hot spots is also explored. Results indicate that beetles intensely infest mature trees with a shift to younger trees over time. Hot-spot locations are most commonly associated with stands composed of 30–80% pine and almost always occur at elevations between 800 m and 1000 m. In the early years of an infestation, hot spots are typically found on warmer (south and west) aspects. As well, relative to non-treatment, any type of treatment reduces the persistence of hot spots the following year.
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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".