Sampling cows to assess lying time for on-farm animal welfare assessment
Bibliographic record
Abstract
The time that dairy cows spend lying down is an important measure of their welfare, and data loggers can be used to automatically monitor lying time on commercial farms. To determine how the number of days of sampling, parity, stage of lactation, and production level affect lying time, electronic data loggers were used to record lying time for 10 d consecutively, at 3 stages of lactation [early: when cows were at 10-40 d in milk (DIM), mid: 100-140 DIM, late: 200-240 DIM] of 96 Holstein cows in tiestalls (TS) and 127 in freestalls (FS). We calculated daily duration of lying, bout frequency, and mean bout duration. We observed complex interactions between parity and stage of lactation, which differed somewhat between tiestalls and freestalls. First-parity cows had higher bout frequency and shorter lying bouts than older cows but bout frequency decreased and mean bout duration increased as DIM increased. We found that individual cows were not consistent in time spent lying between early and mid lactation (Pearson coefficient, TS: r = 0.1, FS: r = 0.2), whereas cows seemed to be more consistent in time spent lying between mid and late lactation (TS: r = 0.7, FS: r = 0.3). For both TS and FS cows, daily milk production was significantly, but slightly negatively, correlated with lying time across the lactation (range, r: -0.2 to -0.4), whereas parity was slightly to moderately positively correlated with mean bout duration across the lactation (r: +0.2 to +0.6) and negatively with bout frequency (r: -0.2 to -0.5). To estimate how the duration of the time sample affected the estimates of lying time subsets of data subsets consisting of 1, 2, 3, 4, 5, 6, 7, 8, and 9 d per cow were created, and the relationship between the overall mean (based on 10 d) and the mean of each subset was tested by regression. For both TS and FS, lying time based on 4 d of sampling provided good estimates of the average 10-d estimate (90% of accuracy). Automated monitoring of lying time has potential as a measure of dairy cow welfare on commercial farms but cows differ greatly in lying time. To obtain a representative measure for the herd, it is necessary to sample cows based on their parity and stage of lactation but probably not milk production level.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".