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Record W2943003919 · doi:10.3390/ani9050206

Effect of Slat and Gap Width of Slatted Concrete Flooring on Sow Gait Using Kinematics Analysis

2019· article· en· W2943003919 on OpenAlexaff
Nicolas Devillers, E. Janvier, Farhoud Delijani, S. Méthot, Kristopher Dick, Qiang Zhang, Laurie Connor

Bibliographic record

VenueAnimals · 2019
Typearticle
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsUniversity of ManitobaAgriculture and Agri-Food Canada
Fundersnot available
KeywordsKinematicsGaitGait analysisMathematicsPhysical medicine and rehabilitationPhysicsMedicineClassical mechanics

Abstract

fetched live from OpenAlex

The housing of gestating sows in groups requires sound information about the adapted design of the pen floor. Slatted concrete floors are commonly used for effective drainage of manure but can cause feet injuries and lameness. In the present study, kinematics were used to characterize the gait of 12 gilts and 12 lame sows walking in a corridor on slatted concrete floors with different combinations of slat (85, 105 or 125 mm) and gap (19, 22 or 25 mm) widths. The nine experimental floors were tested with slats in the perpendicular and parallel orientation to the direction of animal walk, according to a duplicated lattice design. Gait parameters were quantified using spatial, temporal and angular kinematics for front and rear limbs. Some parameters were significantly affected by the treatments (p < 0.05), but the effects differed between gilts and lame sows and between slat orientations. Gap width had a significant effect on parameters such as back angle, stride length, foot height, and carpal and tarsal joint angle amplitudes. Slat width significantly affected parameters such as foot height, and carpal and tarsal joint angle amplitudes. Comparisons of the different combinations of slat and gap widths revealed that slats with a width of 105–125 mm and gap width of 19–22 mm had the least effect on the gait characteristics of the gilts and sows.

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 score0.546
Threshold uncertainty score0.622

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.053
GPT teacher head0.362
Teacher spread0.308 · 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

Citations9
Published2019
Admission routes1
Has abstractyes

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