The Reliability of Genitalia Morphology to Monitor the Spread of the Invasive Winter Moth (Lepidoptera: Geometridae) in Eastern North America
Bibliographic record
Abstract
Winter moth, Operophtera brumata L. (Lepidoptera: Geometridae), causes widespread defoliation in both its native and introduced distributions. Invasive populations of winter moth are currently established in the United States and Canada, and pheromone-baited traps have been widely used to track its spread. Unfortunately, a native species, the Bruce spanworm, O. bruceata (Hulst), and O. bruceata × brumata hybrids respond to the same pheromone, complicating efforts to detect novel winter moth populations. Previously, differences in measurements of a part of the male genitalia called the uncus have been utilized to differentiate the species; however, the accuracy of these measurements has not been quantified using independent data. To establish morphological cutoffs and estimate the accuracy of uncus-based identifications, we compared morphological measurements and molecular identifications based on microsatellite genotyping. We find that there are significant differences in some uncus measurements, and that in general, uncus measurements have low type I error rates (i.e., the probability of having false positives for the presence of winter moth). However, uncus measurements had high type II error rates (i.e., the probability of having false negatives for the presence of winter moth). Our results show that uncus measurements can be useful for performing preliminary identifications to monitor the spread of winter moth, though for accurate monitoring, molecular methods are still required. As such, efforts to study the spread of winter moth into interior portions of North America should utilize a combination of pheromone trapping and uncus measurements, while maintaining vouchers for molecular identification.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".