Short communication: The effects of regrouping in relation to fresh feed delivery in lactating Holstein cows
Bibliographic record
Abstract
This study tested whether separating regrouping from the time of fresh feed delivery mitigated the effects of regrouping on cow behavior and milk production. Cows (n = 26) were individually introduced into a stable group of 11 animals/pen fed twice daily. Animals were randomly assigned to early regrouping (at 0300 h, approximately 10.5 h after fresh feed delivery and 3.5 h before the next fresh feed delivery) and late regrouping (between 0630 and 0730 h, coinciding with access to fresh feed). For 3 d, starting immediately after regrouping, video recordings continuously monitored feeding and perching (i.e., standing with the 2 front feet in the lying stall) behavior and displacements at the feed bunk. Data loggers were used to quantify lying time and the number of standing bouts; milk production was automatically recorded at each milking. Daily feeding and lying times and the number of standing bouts per day did not differ between treatments or experimental days. Daily perching time and the number of displacements at the feed bunk did not differ between treatments but decreased with experimental day. Average milk production on d 2 and 3 after regrouping (30.6 ± 1.5 kg/d) was lower than during the 3 d before regrouping (32.3 ± 1.5 kg/d), but we observed no effect of treatment on this decline. We conclude that regrouping at a time not associated with fresh feed delivery does not mitigate the negative effects of regrouping.
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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".