A decision framework for hemlock woolly adelgid management: Review of the most suitable strategies and tactics for eastern Canada
Bibliographic record
Abstract
The invasive hemlock woolly adelgid (HWA) has decimated hemlock stands across much of the eastern United States, and presents a significant threat to all eastern hemlock in Canada across Ontario, Quebec, New Brunswick, Nova Scotia, and Prince Edward Island, especially since the recent detection of its widespread establishment in southwest Nova Scotia. The spread and rising infestation level and impacts of HWA in this region serve as a warning for forest managers across eastern Canada to develop appropriate management plans and priorities. The HWA decision framework presented here aims to prepare forest managers in eastern Canada for the decisions and challenges that they will face, from prevention, detection, and control, to hemlock ecosystem restoration and management program evaluation. We review the strategies and tactics that are currently available, that are being developed, and that show the most promise to date. Given the nature of HWA, the long-term outlook for eastern hemlock in Canada will likely feature HWA as a component of hemlock ecosystems across much of the region, necessitating a comprehensive, adaptive management program to mitigate its ecosystem consequences.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".