The Push-Pull Tactic for Mitigation of Mountain Pine Beetle (Coleoptera: Curculionidae) Damage in Lodgepole and Whitebark Pines
Bibliographic record
Abstract
In an attempt to improve semiochemical-based treatments for protecting forest stands from bark beetle attack, we compared push-pull versus push-only tactics for protecting lodgepole pine (Pinus contorta Douglas ex Loudon) and whitebark pine (Pinus albicaulis Engelm.) stands from attack by mountain pine beetle (Dendroctonus ponderosae Hopkins) in two studies. The first was conducted on replicated 4.04-ha plots in lodgepole pine stands (California, 2008) and the second on 0.81-ha plots in whitebark pine stands (Washington, 2010). In both studies, D. ponderosae population levels were moderate to severe. The treatments were 1) push-only (D. ponderosae antiaggregant semiochemicals alone); 2) push-pull (D. ponderosae antiaggregants plus perimeter traps placed at regular intervals, baited with four-component D. ponderosae aggregation pheromone); and 3) untreated controls. We installed monitoring traps baited with two-component D. ponderosae lures inside each plot to assess effect of treatments on beetle flight. In California, fewer beetles were collected in push-pull treated plots than in control plots, but push-only did not have a significant effect on trap catch. Both treatments significantly reduced the rate of mass and strip attacks by D. ponderosae, but the difference in attack rates between push-pull and push-only was not significant. In Washington, both push-pull and push-only treatments significantly reduced numbers of beetles caught in traps. Differences between attack rates in treated and control plots in Washington were not significant, but the push-only treatment reduced attack rates by 30% compared with both the control and push-pull treatment. We conclude that, at these spatial scales and beetle densities, push-only may be preferable for mitigating D. ponderosae attack because it is much less expensive, simpler, and adding trap-out does not appear to improve efficacy.
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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".