Oviposition Traps to Survey Eggs of <I>Lambdina fiscellaria</I> (Lepidoptera: Geometridae)
Bibliographic record
Abstract
Outbreaks of the hemlock looper, Lambdina fiscellaria (Gueneé), are characterized by rapid increase and patchy distribution over widespread areas, which make it difficult to detect impending outbreaks. This is a major problem with this insect. Population forecasting is based on tedious and expensive egg surveys in which eggs are extracted from 1-m branches; careful observation is needed to avoid counting old unhatched eggs of previous year populations. The efficacy of artificial substrates as oviposition traps to sample hemlock looper eggs was tested as a means of improving outbreak detection and population forecasting. A white polyurethane foam substrate (1,095 lb/ft3) used with the Luminoc insect trap, a portable light trap, was highly efficient in sampling eggs of the hemlock looper. Foam strips placed on tree trunks at breast height were less efficient but easier and less expensive to use for the establishment of extensive survey networks. Estimates based on oviposition traps were highly correlated with those obtained from the 1-m branch extraction method. The oviposition trap is a standard, inexpensive, easy, and robust method that can be used by nonspecialists. This technique makes it possible to sample higher numbers of plots in widespread monitoring networks, which is crucial for improving the management of hemlock looper populations.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".