Mountain pine beetle population sampling: inferences from Lindgren pheromone traps and tree emergence cages
Bibliographic record
Abstract
Lindgren pheromone traps baited with a mountain pine beetle (Dendroctonus ponderosae Hopkins (Coleoptera: Curculionidae, Scolytinae)) lure were deployed for three consecutive years in lodgepole pine stands in central Idaho. Mountain pine beetle emergence was also monitored each year using cages on infested trees. Distributions of beetles caught in pheromone traps and emergence cages were compared. Each year, mountain pine beetle emergence from infested trees occurred within a 30-d period, although beetles were caught in pheromone traps over a period as long as 130 d. A large proportion of the total number of beetles caught in pheromone traps occurred prior to and following peak emergence from infested trees. Beetles caught in pheromone traps during the main emergence period from infested trees had greater whole-body lipids compared to beetles caught early and late in the flight season. Low lipid content of beetles caught before and after the main emergence period could be the result of a long-distance flight caused by fewer sources of pheromone attraction on the landscape and (or) some proportion of reemerged parents in the sample. Results suggest that pheromone traps disproportionately sample mountain pine beetle populations and that natural pheromone sources may influence the number and timing of beetles caught in synthetically baited traps.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".