A bioeconomic analysis of objective-based management options for late-stage emerald ash borer (Coleoptera: Buprestidae) infestations
Bibliographic record
Abstract
Following its North American introduction, the emerald ash borer (Agrilus planipennis Fairmaire) (Coleoptera: Buprestidae) (EAB) has devastated ash populations (Fraxinus Linnaeus) (Oleaceae), largely extirpating the genus from infested regions. Previous cost-benefit analyses of EAB management options, including insecticidal injections, preemptive removals, and replanting, have examined early-stage infestations. This study tests options for late-stage EAB management based on ecological and economic objectives. We parameterized management decisions to evaluate tree counts, basal area, and urban forest value under 7 management options, varying if and when ash trees were injected, removed, and replanted with non-ash species. The simulation is applied to the remaining ash population in Mississauga, Ontario where tree coring and annual assessments determined that injected trees have reduced growth rates and are declining in condition. The results demonstrate that injections help preserve the ash population, maximize basal area, minimize spikes in annual costs, and reduce cumulative costs earlier in the 20-yr study period. However, long-term cost reduction is achieved through ceasing injections and removing ash as they die from EAB. Maintaining tree counts and maximizing net value is achieved through proactive replanting and winding down basal injections, coupled with a slow rate of removal, ultimately bringing the SLow Ash Mortality approach to a close.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".