Softer, Higher-Friction Flooring Improves Gait of Cows With and Without Sole Ulcers
Bibliographic record
Abstract
We studied dairy cows (n = 30) walking on concrete and on a soft, high-friction composite rubber surface to examine how flooring influenced gait and how this differed for cows with hoof lesions. Cows had hooves trimmed 9 wk after the trial and were classified as either with or without sole ulcers. Video recordings of the cows while walking were digitized using motion analysis software to calculate stride variables (length, height, overlap, duration, proportion of triple support, and speed). Gait was scored by a subjective scoring system (1 = sound to 5 = severely lame) and by a continuous visual analog scale for each of 7 gait attributes. Cows with sole ulcers walking on a composite rubber surface had longer strides (156.9 +/- 2.6 vs. 149.6 +/- 2.6 cm), higher stride heights (9.7 +/- 0.3 vs. 8.8 +/- 0.3 cm), more stride overlap (0.4 +/- 2.0 vs. -4.3 +/- 2.0 cm), shorter periods of triple support (3 legs in ground contact; 68.6 +/- 2.0 vs. 73.8 +/- 2.0%), walked faster (1.22 +/- 0.04 vs. 1.17 +/- 0.04 m/s) and had lower overall gait scores (2.9 +/- 0.1 vs. 3.1 +/- 0.1), better tracking-up (19 +/- 2 vs. 24 +/- 2), better joint flexion (29 +/- 2 vs. 33 +/- 2), more symmetric steps (31 +/- 3 vs. 36 +/- 3), and less reluctance to bear weight on their legs (12 +/- 2 vs. 16 +/- 2) compared with walking on concrete. Similar results were found for cows without sole ulcers. Most of the subjective gait measures could distinguish between cows with and without sole ulcers, but this was not the case for kinematic measures other than stride height. Cows with higher gait scores (more severe lameness) showed the greatest improvement in stride length (r = -0.51), triple support (r = 0.59), swing duration (r = -0.44), overall gait score (r = 0.46), and reluctance to bear weight (r = 0.66) when walking on the rubber surface compared with cows with lower gait scores. These results indicate that rubber flooring provides a more secure footing and is more comfortable to walk on, especially for lame cattle.
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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 | 0.000 |
| 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".