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Record W2895783088 · doi:10.1371/journal.pone.0206037

Inertial properties of the German Shepherd Dog

2018· article· en· W2895783088 on OpenAlexafffund
O. Yvette Jones, Silvia Raschke, Philip Riches

Bibliographic record

VenuePLoS ONE · 2018
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Orthopedics and Neurology
Canadian institutionsBritish Columbia Institute of Technology
FundersBritish Columbia Institute of Technology
KeywordsGermanMoment of inertiaBreedGerman Shepherd DogInertial frame of referenceBiomechanicsRehabilitationPhysical medicine and rehabilitationInertiaMathematicsMedicineComputer scienceOrthodonticsAnatomyPhysical therapySurgeryBiologyGeographyPhysicsAnimal scienceClassical mechanicsArchaeology

Abstract

fetched live from OpenAlex

One of the most popular dog breeds deployed by both the police and military has been the German Shepherd yet little is known about the morphology or body segment parameters of this breed. Such measures are essential for developing biomechanical models which, in turn, may guide clinicians in developing surgical interventions, injury treatment and prevention procedures. This paper provides a complete set of body segment parameters and inertial properties for the German Shepherd. Morphometric measures and 3-dimensional inertial properties, including mass, centre of mass, moment of inertia and volume, were measured from 17 segments from 6 German Shepherd police service dog cadavers. Using whole body mass and geometric modelling, 11 regression equations were developed for predicting segment masses, and 33 equations were developed for predicting moments of inertia. Using these data, inverse dynamic analyses may be applied in future investigations of canine mechanics, guiding surgical procedures, rehabilitation and training especially for the German Shepherd breed but potentially for other breeds too.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.124
Threshold uncertainty score0.332

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 head0.147
GPT teacher head0.293
Teacher spread0.146 · 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 designBench or experimental
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

Citations10
Published2018
Admission routes2
Has abstractyes

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