Survival of the Douglas-Fir Beetle in Peeled and Unpeeled Logs and in Stumps
Bibliographic record
Abstract
Abstract The Douglas-fir beetle (Dendroctonus pseudotsugae Hopkins) can cause significant mortality to mature Douglas-fir trees (Pseudotsuga menziesii (Mirb.) Franco) during epidemics. Treatment methods are required to reduce local beetle populations to less-damaging levels. We conducted a study to compare the effect on beetle survival of peeling bark from infested logs at two times of year. By Aug., all beetles in bark from logs peeled in July were dead compared with 155.2 beetles/m2 bark in unpeeled logs. In bark from logs peeled at the end of Aug. and left over winter, there were 3.4 beetles/m2 of bark surface compared with 62.3/m2 in unpeeled logs. It was concluded that peeling logs reduces beetle populations, particularly if done early in the summer. We also examined beetle survival in stumps over winter and found that a mean of 70.4 beetles/stump, or 125.6/m2 of stump surface survived winter. It is estimated that it would take beetles emerging from 24 stumps to kill a tree. West. J. Appl. For. 20(3):149–153.
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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 | 0.000 |
| 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".