Short communication: Rumination and feeding behavior before and after calving in dairy cows
Bibliographic record
Abstract
The objectives of the current study were to describe changes in rumination and feeding behavior around calving. Rumination time, feeding time, and dry matter intake were monitored in 11 freestall-housed cows from 96 h before to 48 h after calving. Data were summarized in 2-h and 24-h periods, adjusting for calving time. Differences between baseline (96 to 24h before calving) and subsequent 24-h periods were evaluated. Compared with baseline, cows spent, on average, 63 ± 30 min/24h less time ruminating and 66 ± 16 min/24h less time feeding in the 24-h period before calving. These behaviors continued to decline during the 24-h period after calving when, compared with baseline, time spent ruminating decreased on average by 133 ± 35 min/24h and time spent feeding decreased by 82 ± 18 min/24h. Dry matter intake tended to decrease by 3.8 ± 1.9 kg in the 24-h period before calving but returned to baseline values in the 24-h following calving. Rumination time and time spent feeding started to decline approximately 4 and 8h before calving, respectively, and increased in the 4 to 6h following calving. Rumination time and time spent feeding show promise as tools to identify cows close to calving.
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 | 0.000 | 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".