Effects of Roughness and Compressibility of Flooring on Cow Locomotion
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 it