Bedding on Geotextile Mattresses: How Much is Needed to Improve Cow Comfort?
Bibliographic record
Abstract
The objective of our study was to evaluate how the amount of sawdust bedding on mattresses affects dairy cattle behavior and preferences. Eleven nonlactating, multiparous cows were housed individually in pens with access to 3 free stalls. Each stall was fitted with a geotextile mattress covered with either 0, 1, or 7.5 kg of kiln-dried sawdust. The experiment began with 7 d of acclimatization to all 3 stalls. Cows were then allowed access to only 1 of the 3 stalls at a time, each for 3 d (restriction phase). At the end of this restriction phase, cows were allowed free access to all 3 stalls for 3 d (free-choice phase). Time spent lying and the number of lying bouts increased significantly with the amount of bedding, from 12.3 +/- 0.53 h lying and 8.5 +/- 0.62 bouts per 24 h on bare mattresses to 13.8 +/- 0.53 h lying and 10.0 +/- 0.62 bouts per 24 h on mattresses with 7.5 kg of sawdust. In addition, the animals spent less time standing with only the front hooves in the stalls when more sawdust was present. When allowed free access to all 3 options, all 11 animals spent a majority of their time lying and standing in the 7.5-kg option. In conclusion, cows preferred mattresses bedded with 7.5 kg of sawdust, on which they spent more time lying down and less time standing with only the front hooves in stalls. These results indicate that more sawdust bedding improves cow comfort in stalls with geotextile mattresses.
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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 | 0.001 | 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".