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Record W4395009110 · doi:10.1111/eth.13467

The impact of urbanization on painted turtle (<i>Chrysemys picta</i>) behaviour

2024· article· en· W4395009110 on OpenAlexafffundabout
Sebastian Blanchett, Audrey Turcotte, Gabriel Blouin‐Demers

Bibliographic record

VenueEthology · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicTurtle Biology and Conservation
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Ottawa
KeywordsPainted turtleTurtle (robot)UrbanizationBiologyGeographyZoologyEcology

Abstract

fetched live from OpenAlex

Abstract Urbanization is a significant driver of the global biodiversity crisis. Turtles are particularly impacted by urbanization because of the vulnerability of riparian habitats to habitat loss and road mortality. Behaviour plays a crucial role in determining the success of urban animals. Behavioural responses to urbanization, however, are rarely studied in turtles even though many turtles are at‐risk and sometimes live in urban areas. Therefore, we evaluated behavioural changes in painted turtles ( Chrysemys picta ) living in wetlands surrounded by a gradient of urbanization. We tested the consistency of painted turtle behaviour in the laboratory and examined the behaviour of painted turtles from 24 wetland sites across an urbanization gradient in Ottawa, Ontario, Canada. We assessed: (i) aggression by measuring the number of active defensive behaviours the turtles performed in response to handling, (ii) boldness by measuring the amount of time the turtles took to emerge from their shells and move from their initial locations in a circular arena and (iii) activity by measuring the amount of time the turtles spent moving in the same circular arena. We found that all behaviours were consistent in the laboratory. We also found that as the level of urbanization increased, turtles were more aggressive and bolder. Urbanization affects painted turtle behaviour, but further research is required to understand the mechanisms responsible and the conservation implications.

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 score0.159
Threshold uncertainty score0.662

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 head0.009
GPT teacher head0.284
Teacher spread0.275 · 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
Published2024
Admission routes3
Has abstractyes

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