Insect parasitoids : a Canadian perspective on their use for biological control of forest insect pests
Bibliographic record
Abstract
An overview of biological control programs against forest insect pests is presented with emphasis on Canadian case histories. The work is examined in the context of conservation, introduction, and augmentation (environmental manipulation and inoculative and inundative release) of insect natural enemies, specifically parasitoids. Historically, studies have concentrated on introductions of exotic parasitoids for control of introduced pests where a number of successes have been recorded. More recent work has entailed inoculative and inundative releases of parasitoids against native pests in an attempt to establish new host-parasitoid relationships to reduce pest populations. These have had limited success and are still being explored by Canadian researchers. Current strategies for using natural enemies are inundative release of native species against native pests and conservation of native parasitoids through selective insecticide timing and forest manipulation. Future directions in biological control programs will include these approaches with increased emphasis on biotechnology and the genetic selection or manipulation of 'desired strains' for release. Continued ecological studies will be essential to ensure a more complete understanding of the interaction between these 'selected parasitoids' and the forest/tree parameters which will influence their success (tri-trophic interactions). These parameters, such as tree vigour (pest resistance), spatial distribution and diversity, will also be targeted for selection to improve the effect of insect natural enemies in the forest environment.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".