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Record W1998728811 · doi:10.3415/vcot-10-03-0039

Kinematic analysis of Labrador Retrievers and Rottweilers trotting on a treadmill

2011· article· en· W1998728811 on OpenAlexaboutno aff
Felipe Stéfan Agostinho, N. S. M. L. Miqueleto, Mirela Ribeiro Verdugo, Letícia Rocha Inamassu, A. O. El‐Warrak, Sheila Canevese Rahal

Bibliographic record

VenueVeterinary and Comparative Orthopaedics and Traumatology · 2011
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Orthopedics and Neurology
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineKinematics

Abstract

fetched live from OpenAlex

OBJECTIVES: The purpose of the study was to evaluate kinematic patterns in clinically normal Labrador and Rottweiler dogs trotting on a treadmill at a constant velocity. METHODS: Ten Labrador Retrievers aged from 2.2 to 5.1 years, and 10 Rottweilers aged from two to 5.9 years were used. A three-dimensional capture system was used to perform analysis of joint kinematics. Kinematic data were collected by use of a triple-camera system. The kinematic study was performed first on the right side of the dog, and then on the left side. Data were analysed by use of a motion-analysis program. Flexion and extension joint angles, angular velocity and angular acceleration were determined for the shoulder, elbow, carpal, hip, stifle, and tarsal joints. RESULTS: Within each group, the differences between the right and left limbs in all variables were not significant. Significant differences occurred between Labradors and Rottweilers in the following categories: angular displacement and minimum angular acceleration of the stifle (Rottweiler >Labrador); angular displacement and maximum angular velocity of the tarsus (Rottweiler >Labrador); minimum angular velocity of the shoulder (Labrador >Rottweiler); angular displacement, maximum angular acceleration, maximum angular velocity, and minimum angular velocity of the elbow (Labrador>Rottweiler); and maximum angle and maximum angular velocity of the carpus (Labrador>Rottweiler). CLINICAL SIGNIFICANCE: Both breeds had similar kinematic patterns, but there were magnitude differences, especially of the elbow and stifle joints. Therefore, each breed should have a specific database.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.295
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
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 head0.250
GPT teacher head0.341
Teacher spread0.092 · 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.

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

Citations63
Published2011
Admission routes1
Has abstractyes

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