Effect of Slat and Gap Width of Slatted Concrete Flooring on Sow Gait Using Kinematics Analysis
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".