A management strategy for emerald ash borer in St. Lawrence Islands National Park
Bibliographic record
Abstract
This article presents a strategy for managing emerald ash borer (EAB) in the St. Lawrence Islands National Park (SLINP), which is located in the United Counties of Leeds and Grenville in eastern Ontario along 100 km of Lake Ontario shoreline and the St. Lawrence River. Background information about EAB and SLINP is followed by an outline of the possible ecological impacts of an EAB infestation on the Park, predictions of where infestations are more likely to occur and how quickly they could spread, whether there will be interactions between EAB-affected stands and invasive vegetation, and whether visitor safety may be compromised. Recommendations to slow the spread of EAB in the Park, prepare for and attempt to mitigate its impacts, contribute to scientific research to better understand it, and conserve ash genetic material include: 1) implement a ban on outside firewood; 2) develop and implement a seed collection program; 3) prioritize invasive vegetation control activities in areas at risk of EAB infestation; 4) establish an EAB detection program for high-traffic areas of the Park; 5) compile a forest resource inventory of the Park and tree inventories of high-traffic areas; 6) conduct branch sampling to determine if EAB is present on Main Duck Island, and if not, consider closing the island to the public; 7) develop and implement a strategic EAB communications plan; and 8) develop a cross-section committee to oversee EAB management.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".