The impact of treatment on mountain pine beetle infestation rates
Bibliographic record
Abstract
The spatial extent of the current mountain pine beetle epidemic in western Canada has highlighted the need to understand the efficacy of treatment strategies. We investigate the effect of five direct-control treatments applied in central British Columbia during a mountain pine beetle epidemic. Using point data from GPS helicopter surveys and kernel density estimators, efficacy was explored through comparisons of infestation intensities at treated locations to randomly selected untreated sites. Small patch and block harvesting treatments showed the clearest signs of reducing infestation intensity; the effects of the fell and burn, monosodium methanearsonate, and pheromone-baited tree treatments were less clear. Through this work, five management guidelines were developed: (1) aggressive treatments can be effective when beetle populations are moderate, although still epidemic; (2) single-tree treatments are only effective when infestation intensities are low or moderate in both the treatment area and surrounding regions; (3) singletree treatments are the most effective when treatments are intensively applied; (4) overall, the more infested trees removed during treatment, the greater the reduction in infestation intensity; and (5) when it is possible to reduce the infestation levels to 2.5 or fewer infested trees per hectare, treatments can be effectively applied.
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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 | 0.003 |
| 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".