Aerosol emitters disrupt codling moth, <i>Cydia pomonella</i>, competitively
Bibliographic record
Abstract
BACKGROUND: Isomate(®) CM MIST aerosol emitters (Pacific BioControl Corp, Vancouver, WA) containing 36 g of codlemone, (E,E)-8,10-dodecadien-1-ol, were deployed at various densities in a commercial apple orchard to generate dosage-response profiles in order to elucidate the behavioral mechanism of disruption. RESULTS: Moth captures decreased asymptotically as Isomate(®) CM MIST densities increased. Data fitting to Miller-Gut and Miller-de Lame plots yielded straight lines, with positive and negative slopes respectively. Catch of male moths decreased from 28 trap(-1) in the control to 0.9 trap(-1) at the highest emitter density. Disruption of >90% was realized at emitter densities greater than 5 units ha(-1) . CONCLUSION: The resulting set of profiles explicitly matched the predictions for competitive rather than non-competitive disruption. Thus, these devices probably disrupt by inducing false-plume following rather than by camouflaging traps and females. The use of 5 MIST units ha(-1) would be necessary to achieve the same level of codling moth control provided by a standard pheromone treatment with passive reservoir dispensers. The need for only a few aerosol emitters, 2.5-5 units ha(-1) , mitigates the cost of labor required to hand-apply hundreds of passive reservoir dispensers; however, a potential weakness in using this technology is that the low deployment density may leave areas of little or no pheromone coverage, where mate finding may occur. This technology is likely to benefit substantially from treatment of large contiguous blocks of crop.
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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".