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Record W2567081216 · doi:10.1186/s13028-016-0265-9

Kinematic and spatiotemporal assessment of habituation to treadmill walking in Labrador retrievers

2016· article· en· W2567081216 on OpenAlexaboutno aff
Pia Gustås, K. Pettersson, Sofia Honkavaara, Anne‐Sofie Lagerstedt, Anna Byström

Bibliographic record

VenueActa veterinaria Scandinavica · 2016
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Orthopedics and Neurology
Canadian institutionsnot available
FundersSvenska KennelklubbenAgria Djurförsäkring
KeywordsTreadmillKinematicsPhysical medicine and rehabilitationSTRIDEHabituationPhysical therapyMedicineAudiologyPhysics

Abstract

fetched live from OpenAlex

BACKGROUND: This study investigated differences in kinematic and spatiotemporal variables in Labrador retrievers during introduction to treadmill walking, with the aim to determine the time required for them to become habituated. Twenty-five healthy, treadmill-naive Labrador retrievers participated in the study. The total angular displacement of the carpus, elbow, tarsus and stifle, as well as stride time and stance duration were calculated from the 3-D tracking of skin mounted reflective markers recorded with 6 infrared light emitting video cameras at 240 Hz. The measurements were done at two walking speeds, 0.78 and 0.96 m/s, in six sessions on the treadmill during two consecutive days. RESULTS: With a 1-2 min acclimatization period following each treadmill speed change, mean values of the study variables were significantly different from the last training session mainly in the first session on the first day. However, between-stride variability was significantly larger for at least one variable even in the fourth session for the slower walking speed, and in the fifth session for the higher walking speed. CONCLUSIONS: The results show the importance of proper pre-training of dogs in locomotion studies at walk using a treadmill, and the need to consider not only variable mean values but also between-stride variability, in order to ensure that dogs are sufficiently accustomed to allow collection of reliable data.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.840
Threshold uncertainty score0.785

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 head0.044
GPT teacher head0.349
Teacher spread0.305 · 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

Citations15
Published2016
Admission routes1
Has abstractyes

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