Distinguishing eastern North American forest moth pests by wing-scale ultrastructure: potential applications in paleoecology
Bibliographic record
Abstract
The use of fossil moth wing scales has recently been introduced as a new method to reconstruct population histories of lepidopterans and provide a proxy for insect disturbance. We investigated the potential for using wing-scale ultrastructure to distinguish between the five most common outbreak species of moth pests in eastern North America: spruce budworm ( Choristoneura fumiferana Clemens), hemlock looper ( Lambdina fiscellaria Guenée), forest tent caterpillar ( Malacosoma disstria Hübner), blackheaded budworm ( Acleris variana Fernie), and jack pine budworm ( Choristoneura pinus Freeman). Using scanning electron images of scales, we made qualitative and quantitative comparisons of morphological traits at the ultrastructural level. We found that hemlock looper and eastern blackheaded budworm scales could be categorically separated from each other and from the three other species. We developed a quadratic discriminant function using measurements of ultrastructure traits that distinguishes scales of the three remaining species with an overall accuracy of 66%. We found that forest tent caterpillar could be well separated based on these traits, but we were less confident in distinguishing the closely related jack pine and spruce budworm. Our method offers potential advantages in scale identification for future studies in paleoecology, while providing the additional advantage of not requiring intact, unfolded, and undamaged scales.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".