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Record W341099056

Description and analysis of vehicle and train collisions with wildlife in Jasper National Park, Alberta Canada, 1951-1999

2001· article· en· W341099056 on OpenAlexaboutno aff
Jim Bertwistle

Bibliographic record

VenueeScholarship (California Digital Library) · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife-Road Interactions and Conservation
Canadian institutionsnot available
Fundersnot available
KeywordsWildlifeNational parkGeographyHabitatPopulationCollisionWildlife refugeEcologyArchaeologyDemographyBiology
DOInot available

Abstract

fetched live from OpenAlex

Problem Statement Jasper National Park (JNP) is located on the East slope of the Canadian Rockies in the province of Alberta, Canada. Two national transportation corridors, the Yellowhead Highway and the Canadian National Railway, each pass through the park. These transportation corridors parallel each other and are located in prime wildlife habitat. Traffic volumes on the Yellowhead Highway are 1.2 million vehicles per year and 12,000 trains per year on the railway. Seventy percent of collisions occur on highways. From 1951-1999-, 3,791 large animals have been killed in collisions with vehicles and trains in JNP. From 1980 to 1999 average yearly transportation-related mortality is 149 animals per year.A variety of wildlife is killed in collisions; however, elk and bighorn sheep make up 53 percent of collisions. This report includes an analysis of collisions involving five ungulates (elk, bighorn sheep, mule deer, white-tailed deer and moose) and four carnivores (wolves, coyotes, grizzly and black bears).Study Objectives • To determine the type of highway vehicle responsible for most collisions. • To analyze and describe changes that have occurred in wildlife populations adjacent to transportation corridors based on collision data. • To analyse and describe temporal and spatial collision trends. • To compare collision on highways and the CNR. • To analyse and describe collisions based on species, age class, and wildlife gender. • To assess the effect of collisions on the elk population adjacent to transportation corridors. • To assess mitigation that has been used to reduce collisions. • To make recommendations to reduce collisions. • To formulate a methodology to address the issue of wildlife mortality on transportation corridors that may be transferable to other locations and National Parks. • To analyze and describe spatial and temporal collision trends for use in developing mitigation to reduce wildlife collisions.Results The majority of vehicles on the Yellowhead Highway are passenger vehicles and transport trucks. Transport trucks make up a disproportional number of collisions versus passenger vehicles. Spatial and temporal collision trends vary depending on the species. Age class and gender collision rates also vary depending on the species and if the collision occurred on highways or the railway. For some species, these trends are both statistically and biologically significant.Regression analysis showed highway traffic volumes are not the single greatest contributor to collision rates. Collision rates are also influenced by changes in wildlife behaviour. These changes vary depending on the species; however, migration to winter ranges adjacent to transportation corridors has a significant influence on collision rates.Using collision data as an indicator of population structure adjacent to transportation corridors shows that changes have occurred in the wildlife structure adjacent to transportation corridors. In some cases, these changes are significant.

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 score0.020
Threshold uncertainty score0.133

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0080.014
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.010
GPT teacher head0.192
Teacher spread0.182 · 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

Citations12
Published2001
Admission routes1
Has abstractyes

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