Combined Effects of Mating Disruption, Insecticides, and the Sterile Insect Technique on Cydia pomonella in New Zealand
Bibliographic record
Abstract
Codling moth was introduced into New Zealand, and remains a critical pest for the apple industry. Apples exported to some markets require strict phytosanitary measures to eliminate the risk of larval infestation. Mating disruption and insecticide applications are the principal means of suppression in New Zealand. We tested the potential for the sterile insect technique (SIT) to supplement these measures to achieve local eradication or suppression of this pest. SIT was trialed in an isolated group of six integrated fruit production (IFP) orchards and one organic orchard (total 391 ha), using sterilized insects imported from Canada, with release by unmanned aerial vehicle and from the ground. Eradication was not achieved across the region, but a very high level of codling moth suppression was achieved at individual orchards after the introduction of sterile moths in combination with mating disruption and larvicides. After six years of releases, catches of wild codling moths at three IFP orchards (224 ha) were 90-99% lower than in 2013-2014, the year before releases began. Catches at three other IFP orchards (129 ha) decreased by 67-97% from the year before releases began (2015-2016), from lower initial levels. At a certified organic orchard with a higher initial population under only organic larvicides and mating disruption, by 2019-2020, there was an 81% reduction in wild moths capture from 2016-2017, the year before releases began.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".