Bibliographic record
Abstract
Abstract The Canadian registration in 2007 of Disrupt SBW Micro-Flakes®, a pheromone-based product for control of spruce budworm,Choristoneura fumiferana(Clemens), paved the way for large-scale trials to test the practicality of mating disruption as a commercial pest management strategy. We review results from field and laboratory experiments on pheromone-based mating disruption of spruce budworm conducted from 1974 to 2008. Application of pheromone from the ground or the air consistently reduced the orientation of males toward pheromone sources. Mating disruption also reduced the mating success of caged or tethered females in 15 of 16 field studies where this parameter was recorded, but had only a limited effect on the mating success of feral females. No consistent difference in the density of egg masses in control and treated plots was observed, which has often been attributed to immigration of gravid females into pheromone-treated plots. Laboratory studies suggest that false-trail following is the predominant mechanism underlying mating disruption in spruce budworm. The enhanced mating success of females with increasing population density suggests that mating disruption should target low-density emergent populations during the initial phase of an outbreak. Constraints that may limit the potential of mating disruption as a management tool include (1) difficulties associated with obtaining accurate sampling estimates at low population density to forecast the onset of outbreaks, (2) potential behavioral adaptations by which females enhance their mating success when the atmosphere is treated with pheromone, and (3) long-range dispersal of females by flight.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.113 | 0.089 |
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".