When and where do dairy cows defecate and urinate?
Bibliographic record
Abstract
The accumulation of urine and feces can be responsible for many cow and environmental problems. Despite this, little is known about the factors affecting defecation and urination. In the first experiment, the occurrence of defecation and urination behaviors of 48 lactating Holstein cows was observed [days in milk (DIM) = 144.7 ± 38.0 d, body weight (BW) = 667.1 ± 72.0 kg, parity = 2.8 ± 2.3] in freestalls over 48 h. In the second experiment, defecation and urination by 29 lactating Holstein dairy cows were observed (DIM = 62 ± 22.1 d, BW = 590 ± 70.0 kg, parity = 2 ± 1.3) in another freestall barn over a period of 5 d and related to cow activity and feeding behavior. In both experiments, based on total occurrence of eliminative behaviors, cows mainly defecated (experiment 1: 33.4 ± 2.0%; experiment 2: 42.3 ± 3.1%) and urinated (experiment 1: 28.2 ± 2.5%; experiment 2: 42.7 ± 4.0%) in the feed alley and while occupying a stall (defecation: experiment 1: 28.5 ± 1.0%; experiment 2: 26.2 ± 3.0%; urination: experiment 1: 42.2 ± 1.5%; experiment 2: 39.9 ± 3.8%). Occupying a stall included lying, standing in the stall, or standing with 2 feet in the stall and 2 feet in the alley. In both experiments, differences were found between cows in frequency of defecation (experiment 1: 9.8 ± 4.2/d, range = 3 to 20; experiment 2: 15.4 ± 4.3/d, range = 6 to 36) and in frequency of urination (experiment 1: 7.0 ± 3.1/d, range = 2 to 18; experiment 2: 9.3 ± 2.8/d, range = 3 to 19). Large differences between cows were observed in the frequency of defecation and urination, but these were not correlated with parity, milk production, BW, DIM, or dry matter intake.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".