Bibliographic record
Abstract
Abstract Chemical insecticides have been an important tool in the management of forest insect pests in Canadian forests. Aerial application of insecticides began in the 1920s and expanded greatly after World War II with the widespread adoption of DDT primarily for the suppression of spruce budworm,Choristoneura fumiferanaClemens (Lepidoptera: Tortricidae), and other defoliating insects. Significant progress was made in the development of new chemical insecticides and formulations including fenitrothion and tebufenozide, as well as technology for the application of insecticides against various insect pests. However, widespread opposition to the use of chemical insecticides in forest management has led to significant reductions in the number of insecticides registered for use in Canadian forests. Developments in the past 20 years have focussed on new insecticides, formulations, and technologies that seek to limit the impacts on non-target organisms and subsequent ecosystem effects. These developments have resulted in significant improvements in the management of traditional management targets, such as the spruce budworm (Choristoneura fumiferana(Clemens); Lepidoptera: Tortricidae) but also the management of invasive species, especially wood-boring beetles (Coleoptera: Buprestidae, Cerambycidae).
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".