Good Handling Practices Have Positive Impacts on Dairy Calf Welfare
Bibliographic record
Abstract
The objective was to evaluate the effects of good handling practices on dairy calf welfare. Forty-eight crossbred dairy calves were assigned to two treatments: conventional handling (CH): calves kept in individual pens, fed milk replacer in buckets without nipples and abruptly weaned; or good handling practices (GHP): calves kept in group pens, fed milk replacer in buckets with nipples, given daily tactile stimulation during feeding, and progressive weaning. Calf welfare was assessed from birth to 120 days of age, based on: health (plasma concentrations of glucose and IgG, and occurrences of diarrhea, pneumonia, tick-borne disease, or death); physiology (heart rate [HR], respiratory rate [RR], and rectal temperature [RT]); behavior (flight distance [FD], latencies for first movement [LM] and to hold the calf in a pen corner [LH], and total time a calf allowed touching [TTT]); and performance indicators (body weight, average daily gain, and weaning success at 70 days of age). Calves in the GHP treatment had a lower HR at 30 days of age, shorter FD and LH, longer TTT, and lower RR and RT than CH (p < 0.05). However, health, deaths and performance indicators did not differ (p > 0.05) between treatments. Based on various indicators, GHP improved dairy calf welfare.
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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".