Laboratory evaluation of two novel strategies to control first‐instar gypsy moth larvae with spinosad applied to tree trunks
Bibliographic record
Abstract
Two strategies for controlling first-instar larvae of gypsy moth (Lymantria dispar (L)), insecticidal bait and contact insecticide applied directly to the tree trunk, were evaluated in the laboratory. Spinosad was selected as a candidate natural-product insecticide that is active both by contact and ingestion. Incorporated into artificial diet-based bait, spinosad was toxic to neonate larvae with a minimal 10-s feeding period, with an LC50 value of 20 (15-26, 95% confidence interval) mg liter-1. It was significant that neonate larvae did not discriminate between spinosad-treated and control diet. Efficacy of diet-based bait in the laboratory, however, was significantly impacted by previous exposure to diet; fed larvae did not stop at the bait and did not incur mortality, as compared to unfed larvae. Oak bark was a suitable substrate from which neonate larvae could contact spinosad residues. Spinosad applied directly to oak bark resulted in significant mortality after 1- and 4-min crawling contact exposure times (LC50 = 24 [20-29, 95% CI] and 8.7 [6.9-11, 95% CI] mg liter-1, respectively) and contact activity persisted for 2 weeks. While contact activity was more potent on glass surfaces than on oak bark, the LC50 values differed only by factors of 2.4 and 3.6, for 1- and 4-min exposures respectively.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".