An artificial delay in emergence influences the number but not the fitness of adult emerald ash borer emerging from infested ash wood
Bibliographic record
Abstract
Abstract Emerald ash borer (EAB), Agrilus planipennis Fairmaire (Coleoptera: Buprestidae), is the most significant invasive forest pest in North America. Laboratory research on this species requires sources of adult and larval insects that are of the same fitness as those present in the wild. Production of adult EAB relies on flushing adults from logs which are subject to cold storage for some period prior to use. The effect of this storage on the number of insects emerging or the fitness of those that emerge has not been investigated. We subjected logs of EAB‐infested white ash, Fraxinus americana L. (Oleaceae), to 7–14 months of cold storage and quantified the number of insects that emerged, the time to emergence, and the body condition of adults as a measure of fitness. Body condition was evaluated using Soxhlet fat extraction and water weight. No published methods for Soxhlet fat extraction were available for this species so we developed extraction protocols. The number of insects emerging decreased with time, but fitness (i.e., fat and water content) did not decrease. Time to emergence did increase but only in the longest storage treatment, whereas a comparison of male vs. female emergence provides evidence for protandry in EAB. Rearing programs for EAB using wood from cold storage should adjust the amount used to produce a given number of insects but the quality of those individuals emerging will not be affected. We suggest that those insects that perished during storage were of lower quality when entering diapause and thus would serve as poor‐quality host in rearing programs for natural enemies of EAB. These data also provide evidence for one pathway of introduction for EAB, suggesting that refrigeration in transit was required for EAB to remain viable and establish its beachhead in North America.
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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 | 0.001 |
| 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".