To Treat or Remove: An Economic Model to Assist in Deciding the Fate of Ash Trees Threatened by Emerald Ash Borer
Bibliographic record
Abstract
A model is presented to assist in deciding the fate of ash trees (Fraxinus spp.) threatened by the arrival of emerald ash borer (Agrilus planipennis Fairmaire) in North America. The model tracks ongoing treatment costs versus one-time costs associated with removal and replacement. All future values are discounted following standard economic practice. For each year over a period of interest, the net treatment gain/loss is calculated, indicating the period of time over which a homeowner would be financially ahead/behind by treating the existing ash tree. The model was populated, with values that may be expected in Canadian conditions, where treatment options are more limited than in the United States. Optional model features include property value premiums, energy savings, runoff and pollution benefits, and ongoing maintenance costs. When these extended benefits and costs are included, positive treatment gains for a medium-sized ash persist for about 17 years. Negative values can be interpreted as a “break-even existence value,” an amount a homeowner would be required to pay in order to protect their ash if various other benefit flows fail to compensate the costs. An interactive version of the model is available online (http://gmaps.nrcan.gc.ca/apm/index.php).
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".