Canadian contributions to forest insect pathology and to the use of pathogens in forest pest management
Bibliographic record
Abstract
Abstract The study of insect pathogens became established as a distinct discipline in the late 1940s. In the ~65 years that followed, forest pest management was the main theatre for the development and practice of insect pathology in Canada. Researchers from the federal government and academic institutions contributed to the growing discipline by acquiring foundational knowledge on taxonomy, mode of action, natural occurrence, and ecological role of key pathogens infecting forest pest insects, covering an array of fungi, Microsporidia, viruses, and bacteria. The ultimate goal was to develop pathogen-based alternatives to synthetic insecticides used in large-scale forest protection programmes throughout eastern Canada. That goal was achieved through the development of baculovirus-based products for control of gypsy moths (Lepidoptera: Erebidae), tussock moths (Lepidoptera: Erebidae), and various sawfly (Hymenoptera) species, which are now in the hands of private industry and poised for growing operational use. The second success was the development of products based onBacillus thuringiensisBerliner (Bacillaceae), which have almost entirely replaced synthetic insecticides in forest protection. We review those successes and other key Canadian contributions to forest insect pathology within the context of emerging digital, molecular, and other technologies, and show how they have altered today’s face of forest pest management in Canada.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".