Preferences of dairy cows for three stall surface materials with small amounts of bedding
Bibliographic record
Abstract
Farmers' concerns about the economy, cost of labor, and hygiene have resulted in reduced use of organic bedding in stalls for dairy cows; however, the reduced use of organic bedding possibly impairs cow comfort. The effects of different stall surface materials were evaluated in an unheated building in which only a small amount of bedding was used. The lying time and preferences of 18 cows using 3 stall surface materials (concrete, soft rubber mat, and sand) were compared. All materials were lightly bedded with a small amount of straw, and the amount of straw added to each stall was measured. The cows only had access to stalls of one surface type while their lying time was observed. Lying times were longest on the rubber mats compared with other surfaces (rubber mat 768; concrete 727; sand 707+/-16 min/d). In a preference test, cows had access to 2 of the 3 types of stalls for 10 d and their stall preference was measured. Cows preferred stalls with rubber mats to stalls with a concrete floor (median 73 vs. 18 from a total of 160 observations per day; interquartile range was 27 and 12, respectively), but showed no preference for sand stalls compared with stalls with a concrete floor or with rubber mats. More straw was needed on sand stalls compared with concrete or mat (638+/-13 g/d on sand, 468+/-10 g/d on concrete, and 464+/-8 g/d on rubber mats). Lying times on bedded mats indicated that mats were comfortable for the cows. If availability or cost of bedding material requires limiting the amount of bedding used, rubber mats may help maintain cow comfort.
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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".