Tail Docking Dairy Cattle: Effects on Cow Cleanliness and Udder Health
Bibliographic record
Abstract
To determine whether tail docking would influence cow cleanliness and udder health in a free-stall system, we monitored milking cows after half the animals in a herd were docked. A sample of 223 docked and 190 undocked cows (reducing to 169 and 105 over the study as cows were dried off) were monitored for 8 wk. Cow cleanliness was scored in two areas: along the spine, and the rump adjacent to the tail at 1, 2, 3, 5, and 8 wk after docking. Cleanliness was evaluated by counting squares that were soiled (0 to 14 on a 5- x 17.5-cm grid) and judging soiling severity on a scale from 0 (clean) to 3 (thickly caked). Udder cleanliness was scored with the same scale (0 to 3) and by counting the number of teats with debris on them. Udder health was assessed by measuring SCC of two milk samples and the number of animals diagnosed as mastitic by the on-farm veterinarian. No treatment differences were found in four measures of cow cleanliness, two measures of udder cleanliness, or udder health. However, cow cleanliness did differ over time, and analysis of a subsample of cows illustrated individual differences in cleanliness.
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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 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".