Short communication: Flooring preferences of dairy cows at calving
Bibliographic record
Abstract
The present study investigated the flooring preference during the 30 h before parturition in Holstein dairy cows housed individually in a maternity pen. Seventeen multiparous cows were moved, on average, 2 d before expected calving date into an individual maternity pen with 3 different flooring surfaces: 10 cm of sand, pebble-top rubber mats, or concrete flooring, each covered with 15 cm of straw. Calving location, lying time, and total time and number of lying bouts on each of the floor types were recorded during 2 periods: precalving (24 to 29 h before calving) and at calving (0 to 5h before calving). Ten cows calved on sand, 6 on concrete, and 1 on the rubber mat. Lying bouts increased during the hours closest to calving, regardless of flooring. The number of lying bouts did not differ between flooring types precalving but cows had more lying bouts on sand and concrete compared with rubber at calving. Cows spent more time lying down on sand and concrete compared with rubber precalving, but lying times did not differ between treatments at calving. Cows that calved on sand spent more time lying on sand at calving compared with the other 2 flooring types. Cows that calved on concrete did not show a flooring preference at calving. These results indicate that rubber mats are the least preferred by dairy cows in the maternity pens, even when covered with a deep layer of straw.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".