Forest Restoration in Southern Ontario's Conservation Areas Impacted by Emerald Ash Borer: A Case Study with Credit Valley Conservation
Bibliographic record
Abstract
Ash tree mortality and removal due to emerald ash borer (Agrilus planipennis Fairmaire) (EAB) has increased forest degradation in parts of North America. The current project uses Credit Valley Conservation (CVC) as a case study to determine effective forest restoration strategies to restore areas impacted by EAB, with the results applicable to areas impacted by EAB throughout North America. CVC's forests, which were already heavily affected by invasive plant species before the introduction of EAB, are now dealing with the loss of overstory ash that is increasing the spread of invasive plant species and leading to further forest degradation. By using GIS to identify priority restoration areas and the literature to outline management strategies, the current project explores the best approach for conservation authorities to manage these EAB-impacted areas and mitigate the negative impacts of invasive plants and loss of overstory ash. Techniques that manage invasive plants, including chemical, biological, and mechanical control, along with the increased planting of native tree species will help restore forests and improve their ecological function. These approaches will also help deal with the impact of climate change and increased human disturbance by generally increasing forest diversity and improving forest resilience.
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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 | 0.000 | 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 | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".