Effects of Mimic(R) bioinsecticide on the species diversity of non-target forest Lepidoptera in an operational spruce budworm (Lepidoptera : Tortricidae: Choristoneura fumiferana Clem.) suppression program in northwestern Manitoba
Bibliographic record
Abstract
A new biochemical insecticide, Mimic [R] (Dow Agrochemicals), has recently been registered in Canada for the control of lepidopteran defoliators in forest ecosystems. The active ingredient, tebufenozide, mimics the insect molting hormone, 20-hydroxyecdysone, in larvae of some species of Lepidoptera inducing a premature molt, causing death. To date there has been only one published study on the effects of an operational spray program that has addressed the effects of Mimic[R] on non-target Lepidoptera in hardwood forest, and none in the boreal forest. Butler et al. (1997) found significant reductions in richness and abundance of non-target, larval macrolepidoptera of a hardwood forest following Mimic[R] application for control of gypsy moth, Lymantria dispar (L.). In 1999 and 2000, Manitoba Conservation applied Mimic[R] to areas of the boreal forest in northwestern Manitoba as part of an operational spruce budworm suppression program. In 2000 and 2001, moths and larvae were collected from twelve study sites within the spray area to determine the effect of Mimic[R] on spruce budworm and non-target Lepidoptera. Three 70 m2 plots were within spray blocks sprayed once with 70g Al in 2.0 L/ha in June of 1999; three 70 m2 were within spray blocks sprayed once in June of 2000 and six were in unsprayed areas...
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 | 0.000 | 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) | 0.001 | 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".