An effective strategy for trapping Douglas‐fir beetle, <i>Dendroctonus pseudotsugae</i> (Col., Scolytidae) using combinations of unbaited and pheromone baited funnel traps
Bibliographic record
Abstract
Experiments were conducted to confirm and quantify earlier observations that unbaited funnel traps in conjunction with pheromone baited funnel traps may substantially increase trap catches of Douglas‐fir beetles, Dendroctonus pseudotsugae. In 12 replicates of one of these experiments during 1998 and 1999, the catches of a single baited control trap were compared to those of a set of four traps where the central trap was baited and a ring of three traps, 1 m from the central trap arranged in a star configuration, were unbaited. In the second experiment conducted during 1999, a ring of three unbaited traps was placed at 2 m and a second at 5 m from the central baited trap. Statistically robust results demonstrated clearly that the configuration of one central baited trap plus three unbaited satellite traps collected twice as many beetles on average over the season compared to the baited control trap. Addition of a second ring of unbaited traps increased collections by only about 20%, but this experiment indicated that trap catches dropped off exponentially with distance from the centre. The number of beetles caught in the baited traps was essentially the same in all 3 arrangements, suggesting that the additional unbaited traps captured beetles that otherwise may not have been captured.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".