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Record W1487656839

Utilizing a Multi-Technique, Multi-Taxa Approach to Monitoring Wildlife Passageways on the Bennington Bypass in Southern Vermont

2007· article· en· W1487656839 on OpenAlexaboutno aff
Mark A Bellis, Scott D. Jackson, Paige S. Warren, Alan O. Thompson

Bibliographic record

VenueeScholarship (California Digital Library) · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife-Road Interactions and Conservation
Canadian institutionsnot available
FundersVermont Agency of TransportationU.S. Forest ServiceUniversity of MontanaFlorida State University
KeywordsWildlifeHabitatGeographyMetapopulationWildlife corridorEnvironmental resource managementEcologyFisheryEnvironmental sciencePopulationBiological dispersalBiology
DOInot available

Abstract

fetched live from OpenAlex

Roadways affect wildlife habitat disproportionate to the area of land they occupy while impacting wildlife directly through direct loss of habitat, road mortality and disruption of movement. Roadways indirectly impact wildlife by isolating populations and disrupting gene flow and metapopulation dynamics. A variety of strategies have been used with mixed success to mitigate the impacts of transportation systems on wildlife. Underpasses are commonly used to facilitate movement of wildlife across roadways in Europe, Australia, Canada and the U.S.Through 2005, 460 terrestrial and 300 aquatic crossing structures have been identified throughout the United States but only a small portion of these crossings have monitoring incorporated into their project design. Most monitoring is limited to usage of the passage structures with little data collected on movement through the adjacent landscape. Monitoring of the passage structures helps determine wildlife use of the structures but is limited in the ability to determine landscape level impacts.A variety of techniques are utilized in monitoring passageway effectiveness, primarily camera traps and track beds. Building on prior studies, the Bennington Bypass project takes a broad, multi – taxa approach to monitoring crossing structures on a newly constructed highway in southern Vermont. We are utilizing a variety of techniques to assess movements of an array of species at the passage structure and in the surrounding landscape.Techniques utilized in our study include: small mammal trapping, track beds/plates, remote camera sensing, snowtracking, road kill surveys, roadside track beds, amphibian recording devices, snake pit tagging and observational studies. We are also using this broad approach to monitoring as an opportunity to test and refine many of the techniques used in the study. By monitoring a wide variety of animal movements rather than focusing exclusively on wildlife use of the passages, we expect to more accurately assess the effectiveness of the mitigation structures. We anticipate that the results from this work will assist in developing monitoring protocols for future studies in Vermont and throughout the United States.

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)0.001
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 score0.391
Threshold uncertainty score0.777

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.032
GPT teacher head0.242
Teacher spread0.210 · 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
Published2007
Admission routes1
Has abstractyes

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