Brisket Boards Reduce Freestall Use
Bibliographic record
Abstract
We examined how the presence of a brisket board influenced cow preference, stall use, and position within the stall. When given a choice between stalls with or without a brisket board, 15 nonlactating cows spent 68% of their time lying in the stalls without a brisket board, indicating that they preferred this option. When 13 cows had access to either stalls with a brisket board or ones without, they spent, on average, 1.2 h/d more time lying down in stalls without a brisket board. Resting cows positioned themselves relatively forward in the stalls in 98 +/- 5% (mean +/- SE) of lying bouts when the brisket board was absent, compared with 67 +/- 5% of bouts when the board was present. Longer cows were more likely than shorter cows to move forward in the stalls without a brisket board. Cows also had longer lying bouts in stalls without the brisket board (absent: 1.7 +/- 0.08; present: 1.5 +/- 0.08 h/bout). Although it seems likely that the brisket board helps keep stalls clean by positioning cows closer to the curb, our results indicate that brisket boards also make stalls less comfortable for cows. Stall features designed to reduce stall maintenance may compromise cow comfort. We suggest that new approaches to cow housing are now required.
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.
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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".