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Record W2734810318 · doi:10.1139/cjz-2016-0286

The effects of roads on habitat selection and movement patterns of the American badger subspecies <i>Taxidea taxus jacksoni</i> in Ontario, Canada

2017· article· en· W2734810318 on OpenAlexafffundvenueabout
Julia Sunga, Josh Sayers, Karl Cottenie, Christopher J. Kyle, Danielle M. Ethier

Bibliographic record

VenueCanadian Journal of Zoology · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife-Road Interactions and Conservation
Canadian institutionsTrent UniversityUniversity of Guelph
FundersOntario Ministry of Natural Resources and ForestryMinistry of Natural ResourcesWorld Wildlife Fund
KeywordsBadgerHabitatEcologyBurrowPopulationGeographyEndangered speciesSpatial ecologyBiologyDemography

Abstract

fetched live from OpenAlex

Road mortality is identified as a threat to American badger (Taxidea taxus (Schreber, 1777)) populations across Canada. Understanding habitat selection and movement in relation to roads is therefore vital to their conservation. Using telemetry data and road-kill locations of badgers in southwestern Ontario, we examined the relationship between habitat selection, movement patterns, and roads at three spatial scales. At the study-area scale, we assessed the effects of habitat attributes on burrow site selection. Several individuals placed their burrows closer to primary highways than expected, suggesting that badgers are not sensitive to human disturbances at this scale. Using straight-line movement trajectories between burrows, we analyzed individual movement patterns within home ranges. All badgers showed some degree of road avoidance, either crossing fewer roads or roads that posed lower mortality risk. At the road-crossing scale, we compared landscape features surrounding road-kill locations to random locations along the same roadway. There was a positive relationship between road-kill locations and number of water-based linear features and higher density of hedgerow cover. Our results provide evidence that badger movement is influenced by roads at multiple scales, which has important implications for managers interested in developing road-mitigation strategies for this endangered population.

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 score0.005
Threshold uncertainty score0.170

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)0.0000.000

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 head0.004
GPT teacher head0.181
Teacher spread0.177 · 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

Citations16
Published2017
Admission routes4
Has abstractyes

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