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Record W613257516

Cost Justification and Examples of Cost-Benefit Analyses of Mitigation Measures Aimed at Reducing Collisions with Large Ungulates in the United States and Canada

2010· article· en· W613257516 on OpenAlexaboutno aff
Marcel P. Huijser, John W. Duffield, Anthony P. Clevenger, Robert Ament, Pat McGowen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife-Road Interactions and Conservation
Canadian institutionsnot available
Fundersnot available
KeywordsUngulateWildlifeFencingCost–benefit analysisGeographyFisheryEnvironmental scienceEcologyHabitatBiology
DOInot available

Abstract

fetched live from OpenAlex

Wildlife-vehicle collisions, especially with deer (Odocoileusspp.), 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. The authors 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, the authors reviewed the effectiveness and costs of 13 mitigation measures considered effective in reducing collisions with large ungulates The authors 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, the authors 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 0.7 moose per km per year (all at 3% discount rate). In addition, the authors calculated the costs associated with large ungulate-vehicle collisions on ten road sections throughout the United States and Canada and compared these to the threshold values. Finally, the authors conducted a more detailed cost analyses for one of these road sections to illustrate that even though the average costs for large ungulate-vehicle collisions per kilometer per year may not meet the thresholds of many of the mitigation measures, specific locations on a road section can still exceed thresholds. While the analyses can be expanded to include other parameters (e.g. the economic value of habitat connectivity or viable wildlife populations), the authors believe the cost-benefit model presented in this paper can be a valuable decision support tool for determining mitigation measures to reduce ungulate-vehicle collisions.

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.323
Threshold uncertainty score0.651

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.020
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.004
Bibliometrics0.0080.007
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.030
GPT teacher head0.273
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2010
Admission routes1
Has abstractyes

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