Session The Economics of Mitigation and Cost-Effective Strategies Abstract COST JUSTIFICATION AND EXAMPLES OF COST-BENEFIT ANALYSES OF MITIGATION MEASURES
Bibliographic record
Abstract
Wildlife-vehicle collisions, especially with deer (Odocoileus spp.), elk (Cervus elaphus) and moose (Alces alces) are numerous and have shown an increasing trend over the last several decades in the United States and Canada. We calculated the costs associated with the average deer- ($6,617), elk- ($17,483) and moose-vehicle collision ($30,760), including vehicle repair costs, human injuries and fatalities, towing, accident attendance and investigation, monetary value to hunters of the animal killed in the collision, and cost of disposal of the animal carcass. In addition, we reviewed the effectiveness and costs of 13 mitigation measures considered effective in reducing collisions with large ungulates We conducted cost-benefit analyses over a 75-year period using discount rates of 1%, 3 % and 7 % to identify the threshold values (in 2007 $) above which individual mitigation measures start generating benefits in excess of costs. These threshold values were translated into the number of deer-, elk-, or moose-vehicle collisions that need to occur per kilometer per year for a mitigation measure to start generating economic benefits in excess of costs. For example, we calculated that wildlife exclusion fencing in combination with large mammal underpasses (one every 2 km) and wildlife jump-outs generates economic benefits if the pre-mitigation collisions are greater than 3.2 deer, 1.2 elk, or
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 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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.002 |
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".