MétaCan
Menu
Back to cohort
Record W2165028932 · doi:10.14430/arctic844

The Effect of Vehicle Traffic on Wildlife in Denali National Park

2000· article· en· W2165028932 on OpenAlexvenueno aff
Shan Burson, Jerrold L. Belant, K.A. Fortier, Warren Tomkiewicz

Bibliographic record

VenueARCTIC · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsnot available
Fundersnot available
KeywordsGrizzly BearsNational parkUrsusOvisWildlifeGeographyPopulationEcologyBiologyDemographyArchaeology

Abstract

fetched live from OpenAlex

We recorded observations of caribou (Rangifer tarandus), grizzly bear (Ursus arctos), Dall sheep (Ovis dalli) and moose (Alces alces) along the Denali National Park and Preserve road corridor during 1995-97. We compared these observations to similar data from previous studies to evaluate the effect of an increase in traffic on the number of animals sighted and their behavior. Between 1972 and 1997, annual visitation to Denali National Park increased from about 45000 to 350000, with attendant increases in traffic on the park road. The mean number of caribou, grizzly bear, and Dall sheep observed did not decline (p > 0.301) from 1973 to 1997. The number of moose observed declined by more than 50% (R² = 0.529, p < 0.001). The estimated population of moose also declined over the same period (R² = 0.374, p = 0.002). The distance from the park road at which caribou and grizzly bears were sighted did not change (p > 0.787), but fewer moose (p < 0.031) were observed within 100 m of the road and fewer sheep (p < 0.011) were observed between 400 and 500 m from the road. Adverse behavioral responses to traffic (e.g., running from vehicles) occurred in less than 1.3% of observations for each species. Increased traffic on the park road apparently has not caused significant changes in abundance, distribution, or behavior of caribou, grizzly bear, Dall sheep, and moose in the park road corridor.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.027
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.005
GPT teacher head0.214
Teacher spread0.208 · 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 teacher head, 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

Citations29
Published2000
Admission routes1
Has abstractyes

Explore more

Same venueARCTICSame topicWildlife Ecology and ConservationFrench-language works237,207