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Record W599164141

Highway 3: Transportation Mitigation for Wildlife and Connectivity

2010· article· en· W599164141 on OpenAlexaboutno aff
Anthony P. Clevenger, Clayton D. Apps, Tracy Lee, Mike Quinn, Dale Paton, Dave Poulton, Robert Ament

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife-Road Interactions and Conservation
Canadian institutionsnot available
Fundersnot available
KeywordsWildlifeWildlife corridorGeographyWildlife conservationPopulationEnvironmental resource managementWildlife managementEcologyEnvironmental science
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 at the northern edge of the Crown of the Continent ecosystem. 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 in the southern Canadian Rockies. The current rate of wildlife–vehicle collisions involving large mammals along Highway 3 has raised concerns among agencies and the public regarding motorist safety. Although highway segments experiencing a high number of these collisions are predominantly found to involve deer, collisions also occur with less common species such as elk, moose, bighorn sheep, grizzly bear, wolf, lynx, bobcat and cougar. The syntheses, field assessments and recommendations described in this report reflect the best available understanding and options for direct mitigation of highway impacts to local populations of large terrestrial wildlife. Although conservation measures at regional and landscape scales are critical in maintaining wildlife population connectivity, the focus of this report is at the finest scale necessary to address Highway 3 impacts on terrestrial wildlife—that of site-specific mitigation of the highway itself.

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 score0.324
Threshold uncertainty score0.771

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)0.0010.000

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 head0.007
GPT teacher head0.223
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 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

Citations3
Published2010
Admission routes1
Has abstractyes

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