Effects of Roughness and Compressibility of Flooring on Cow Locomotion
Bibliographic record
Abstract
We examined the effects of roughness and degree of compressibility of flooring on the locomotion of dairy cows. We observed 16 cows walking down specially constructed walkways with materials that differed in surface roughness and degree of compressibility. Use of a commercially available soft rubber flooring material decreased slipping, number of strides, and time to traverse the corridor. These effects were most apparent at difficult sections of the corridor, such as at the start, at a right-angle turn, and across a gutter. Covering the walkway with a thin layer of slurry increased frequency of slipping, number of strides, and time taken to traverse the walkway. Effects of adding slurry were not overcome by increasing surface roughness or compressibility. Placing more compressible materials under a slip-resistant material reduced the time and number of steps needed to traverse the corridor but did not reduce slips, and the effects on cow locomotion varied nonlinearly with the degree of compressibility of the floor. Use of commercially available rubber floors improved cow locomotion compared with concrete floors. However, standard engineering measures of the floor properties may not predict effects of the floor on cow behavior well. Increasing compressibility of the flooring on which cows walk, independently of the roughness of the surface, can improve cow locomotion.
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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 | 0.002 |
| 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".