Addition of terpenoids to pear ester plus acetic acid increases catches of codling moth (Lepidoptera: Tortricidae)
Bibliographic record
Abstract
Abstract Field studies were conducted to evaluate new kairomone blends in combination with pear ester ( E,Z )‐2,4‐ethyl decadienoate (PE) and acetic acid (AA) for their attraction of male and female codling moth, Cydia pomonella (L.), in apple, Malus domestica Borkhausen. The addition of decanal to either AA or PE alone significantly increased total and female moth catches. However, the addition of decanal did not improve the attraction of PE + AA. The addition of either the pyranoid (PyrLOX) or furanoid (FurLOX) linalool oxide but not linalool (LOL) increased moth catches with PE but did not increase catches with PE + AA. Similarly, the addition of PyrLOX plus decanal did not improve PE + AA. The addition of ( E )‐4,8‐dimethyl‐1,3,7‐nonatriene (DMNT) to either AA, PE + AA or PE + AA+decanal did not significantly increase moth catches. However, the addition of PyrLOX to traps with PE + AA and DMNT (4‐component lure) significantly increased moth catches compared with PE + AA alone or any of the ternary blends of these volatiles. Females accounted for 60%–80% of the total catch with this 4‐component lure. The 4‐component blend with PyrLOX was a more attractive lure than similar blends that substituted LOL, or a binary blend of LOL and FurLOX for PyrLOX. The 4‐component blend caught nearly fourfold more total and female moths than the purported attractant N‐butyl sulphide when it was used in combination with PE + AA. These results indicate that significant improvements in monitoring, mating disruption and mass trapping of codling moth are possible. Further studies are needed to assess the new attractive blend's effectiveness in combination with sex pheromone lures and to evaluate whether other host plant volatiles can be added or substitute for DMNT or LOX when used in combination with PE + AA.
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 | 0.000 | 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) | 0.002 | 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".