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Record W1952777821 · doi:10.1071/zo15007

Walking on five legs: investigating tail use during slow gait in kangaroos and wallabies

2015· article· en· W1952777821 on OpenAlexaff
Rebekah Dawson, Natalie M. Warburton, Hazel L. Richards, Nick Milne

Bibliographic record

VenueAustralian Journal of Zoology · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBat Biology and Ecology Studies
Canadian institutionsDawson College
FundersGlaxoSmithKline
KeywordsGaitBiologyHindlimbMacropusGait analysisAnatomyPelvisTerrestrial locomotionPhysical medicine and rehabilitationZoologyMedicineMarsupialPhysiology

Abstract

fetched live from OpenAlex

Pentapedal locomotion is the use of the tail as a fifth leg during the slow gait of kangaroos. Although previous studies have informally noted that some smaller species of macropodines do not engage in pentapedal locomotion, a systematic comparative analysis of tail use during slow gait across a wide range of species in this group has not been done. Analysis of relative movement of the pelvis, tail, and joint angles of the lower limbs during slow gait, using 2D landmark techniques on video recordings, was carried out on 16 species of Macropodinae. We also compared the relative lengthening of the tibia using crural index (CI) to test whether hindlimb morphology was associated with pentapedal locomotion. Pentapedal locomotion was characterised by three features: the presence of the ‘tail repositioning phase’, the constant height of the pelvis and the stationary placement of the distal tail on the ground during the hindlimb swing phase. The mean CI of pentapedal species was significantly greater than that of non-pentapedal species (1.71 versus 1.36; P < 0.001). This lends support to the hypothesis that the use of pentapedal locomotion is associated with the relative lengthening of the hindlimb, which in turn is associated with body size and habitat preference within the Macropodinae.

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)0.001
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 score0.012
Threshold uncertainty score0.215

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.065
GPT teacher head0.251
Teacher spread0.185 · 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

Citations27
Published2015
Admission routes1
Has abstractyes

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