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Record W1854465000 · doi:10.1111/bij.12000

Moving in the real world: tortoises take the plunge to cross steep steps

2013· article· en· W1854465000 on OpenAlexaff
Ana Golubović, Dragan Arsovski, Rastko Ajtić, Ljiljana Tomović, Xavier Bonnet

Bibliographic record

VenueBiological Journal of the Linnean Society · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicTurtle Biology and Conservation
Canadian institutionsInstitute for Biological Sciences
Fundersnot available
KeywordsTortoiseBiologyHabitatEndangered speciesContext (archaeology)EcologyBoldnessEnergeticsJump

Abstract

fetched live from OpenAlex

Despite exhibiting low velocity and limited agility, many tortoises undertake large scale movements and must overcome various obstacles, notably in populations living in hilly or rocky habitats. Although crucial, studies exploring how tortoises move in complex and irregular environments are scarce. In this context, we examined an important behavioural trait: how tortoises (Testudo hermanni) deal with step-like obstacles. In their natural habitat, individuals were positioned in a challenging situation: they were placed on a bench approximately 50 cm high, and were observed over a 10-min period. We compared the behaviour of the tortoises (taking a risk to ‘jump’ or waiting) from two populations living in contrasted habitats: flat versus rugged (crisscrossed by cliffs and rocky steps). Individuals from the flat habitat were reluctant to jump, whereas most tortoises from the rugged habitat jumped. Immature tortoises were less willing to jump compared to larger and more experienced adults. These results suggest that challenging habitats increase boldness. In addition to fundamental findings, these results may have conservation value and assist in improving translocation strategies for endangered tortoise populations.

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)0.001
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 score0.011
Threshold uncertainty score0.412

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.029
GPT teacher head0.267
Teacher spread0.238 · 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

Citations17
Published2013
Admission routes1
Has abstractyes

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