Lethal Trap Trees and Semiochemical Repellents as Area Host Protection Strategies for Spruce Beetle (Coleoptera: Curculionidae, Scolytinae) in Utah
Bibliographic record
Abstract
We tested lethal trap trees and repellent semiochemicals as area treatments to protect host trees from spruce beetle (Dendroctonus rufipennis Kirby) attacks. Lethal trap tree treatments ("spray treatment") combined a spruce beetle bait with carbaryl treatment of the baited spruce. Repellent treatments ("spray-repellent") combined a baited lethal trap tree within a 16-m grid of MCH (3-methylcyclohex-2-en-1-one) and two novel spruce beetle repellents. After beetle flight, we surveyed all trees within 50 m of plot center, stratified by 10-m radius subplots, and compared attack rates to those from baited and unbaited control plots. Compared to the baited controls, spruce in the spray treatment had significantly reduced likelihood of a more severe attack classification (e.g., mass-attacked over strip-attacked or unsuccessful-attacked over unattacked). Because spruce in the spray treatment also had significantly heightened probability of more severe attack classification than those in the unbaited controls, however, we do not recommend lethal trap trees as a stand-alone beetle suppression strategy for epidemic beetle populations. Spruce in the spray-repellent treatment were slightly more likely to be classified as more severely attacked within 30 m of plot center compared to unbaited controls but, overall, had reduced probabilities of beetle attack over the entire 50-m radius plots. The semiochemical repellents deployed in this study were effective at reducing attacks on spruce within treated plots despite the presence of a centrally located spruce beetle bait. Further testing will be required to clarify operational protocols such as dose, elution rate, and release device spacing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".