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Record W650442212

Does Wildlife Transportation Mitigation Make Cents: A Case Study of Highway 3 in the Crowsnest Pass in the Southern Canadian Rockies

2012· article· en· W650442212 on OpenAlexaboutno aff
Tracy Lee, Anthony P. Clevenger, Clayton D. Apps, Dean Paton, Mike Quinn, Dave Poulton, Robert Ament

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife-Road Interactions and Conservation
Canadian institutionsnot available
Fundersnot available
KeywordsUngulateWildlifeCarnivoreGeographyWildlife conservationWildlife managementEcologyHabitatPredationBiology
DOInot available

Abstract

fetched live from OpenAlex

The Highway 3 transportation corridor, including land use and development adjacent to the highway, has been identified as a major challenge to maintaining wildlife connectivity in the southern Canadian Rockies. Highway 3 is a two-lane, east–west highway supporting 6,000 to 9,000 vehicles per day traveling over the Continental Divide at Crowsnest Pass. Wildlife – Vehicle Collisions (WVC’s) involving large mammals along Highway 3 predominantly involve deer; collisions also occur with less common species such as elk, moose, bighorn sheep, grizzly bear, wolf, lynx and cougar. A cost–benefit analysis was conducted using annual rates of WVCs for 31 mitigation emphasis sites identified along Highway 3 in British Columbia and Alberta. Mitigation emphasis sites (MES) were identified based on a synthesis of carnivore and ungulate use of the landscape surrounding Highway 3, the site’s local and regional conservation value, development status of land adjacent to the MES, and the highway mitigation potential. At each MES an analysis of wildlife vehicle collision mortality data was conducted. The number of collisions per kilometer per year involving deer, elk, moose and bighorn sheep were summarized at each mitigation emphasis site and the total cost of the ungulate– vehicle collisions (UVCs) was compiled. Using the UVC rates at each MES, the annual costs of the UVCs were then derived based on each ungulate species’ average cost per collision (i.e., human fatalities and injuries, vehicle damage): deer ($6,617), elk ($17,483), moose ($30,760) and bighorn sheep ($6,617). In British Columbia, total annual costs of UVCs at MES ranged from a low of $1,323 to $28,329 (includes 23 MES). In Alberta, total annual costs of UVCs varied from a low of $6,617 to a high of $31,405 (includes 14 MES)(all figures in 2007 Canadian dollars). A recent cost–benefit analysis for a variety of highway mitigation measures across North America found the average cost of building and maintaining a wildlife underpass with fencing and jumpouts (escape ramps for wildlife) is $18,123 km/yr. Although underpasses are often considered an “expensive” infrastructure investment for wildlife, nearly onethird of the monetary costs for the sites in British Columbia were estimated in excess of $18,123 km/yr and half of the MES in Alberta had estimated annual costs in excess of this threshold number. These costs indicate many of the MES along Highway 3 in the study area are excellent candidates for underpasses or other infrastructure investments. Further, if the underreporting of WVCs were accounted for, then investment in mitigation at even more sites would have been considered cost effective for using infrastructure investments. Focusing highway mitigation efforts in these areas could improve motorist safety, reduce wildlife mortalities, improve habitat linkage and animal movements across Highway 3 and be cost effective.

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)0.001
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.256

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0090.002
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.229
Teacher spread0.217 · 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

Citations0
Published2012
Admission routes1
Has abstractyes

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