Short communication: Effects of bedding quality on the lying behavior of dairy calves
Bibliographic record
Abstract
The lying behavior of adult cattle is strongly affected by characteristics of the lying surface, but no previous work has assessed the effects of lying surface for dairy calves. We evaluated how the lying behavior of dairy calves changed when calves were provided sawdust-bedded versus concrete lying surfaces, and in response to variation in dryness of the sawdust bedding. Five Holstein calves, approximately 2-wk old, were individually housed in pens with half of the lying surface bedded with kiln-dried sawdust [90% dry matter (DM)] and the other half with wet bedding varying in DM at 4 levels (74, 59, 41, and 29%) or bare concrete. All calves were tested on all 5 treatments, with treatment order assigned using a 5 × 5 Latin square. Total lying time averaged 17.2 ± 0.1 h/d and did not vary with treatment, but time lying on the wet bedding decreased from 5.3 ± 1.1 h/d at 74% DM to almost zero at 29%. Lying times on the side of the pen with dry bedding varied from 12.2 ± 1.2 h/d (when the wet bedding was 74% DM) to 16.8 ± 1.2 h/d (at 29% DM). Standing times were higher on the dry than the wet bedding (2.6 vs. 1.7 ± 0.1 h/d) but did not change across the range of bedding DM tested on the wet side. No calves ever lay down on the bare concrete. In conclusion, dairy calves showed clear preference for drier sawdust bedding and aversion to concrete lying surfaces, indicating that access to soft and dry bedding is important for growing calves.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".