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Record W2116472609 · doi:10.1670/12-176

Blanding's Turtles (<i>Emydoidea blandingii</i>) Avoid Crossing Unpaved and Paved Roads

2014· article· en· W2116472609 on OpenAlexafffundabout
Catherine L. Proulx, Gabrielle Fortin, Gabriel Blouin‐Demers

Bibliographic record

VenueJournal of Herpetology · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife-Road Interactions and Conservation
Canadian institutionsUniversity of Ottawa
FundersNature Conservancy of CanadaNature ConservancyUniversity of Ottawa
KeywordsBiologyHabitatHabitat fragmentationEcologyTurtle (robot)Fragmentation (computing)Habitat destructionGeography

Abstract

fetched live from OpenAlex

Fragmentation of natural landscapes by linear anthropogenic features, such as roads, has several negative consequences, including decreasing connectivity between habitats, inhibiting animal movements, and isolating populations. Roads limit animal movements through behavioral avoidance and mortality during crossing attempts. We investigated the impact of a road network on the movement patterns of Blanding's Turtles (Emydoidea blandingii) in Québec, Canada. We tested the hypothesis that roads act as a barrier to movements. We monitored 52 Blanding's Turtles (22 females, 24 males, and 6 juveniles) via radiotelemetry during their active season from May to August 2010. Road avoidance was quantified for each individual by comparing the number of inferred road crossings with the number of expected road crossings predicted by 1,000 movement path randomizations. Overall, Blanding's Turtles significantly avoided crossing roads. Roads were a significant barrier to movement for 3–6 of the 52 turtles, and an individual's tendency to cross roads was not influenced by its sex or by the road surface (unpaved or paved). Preserving demographic and genetic connectivity of animal populations separated by roads is a major conservation challenge for species at risk such as the Blanding‘s Turtle.

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 score0.232
Threshold uncertainty score0.520

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 head0.005
GPT teacher head0.213
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

Citations19
Published2014
Admission routes3
Has abstractyes

Explore more

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