Enhancing movement opportunity to support behavioral needs for movement-restricted cattle through different conditions of access to exercise
Bibliographic record
Abstract
Intensification in animal production has led to increased indoor confinement for animals, limiting opportunities to meet some behavioral needs such as exploration and locomotion. This poses a crucial question as to how these restrictions can be alleviated by providing additional space designed with that specific purpose in mind. Working with cows housed in tie-stalls, our study aimed to: (1) quantify how providing an exercise area outside the normal housing environment affects locomotor behavior; (2) evaluate how exercise access conditions can contribute to increase locomotor benefits for animals; (3) investigate cows' time budgets during exercise access. Six trials involving different exercise access conditions (indoor vs. outdoor, outing duration, exercise area size, type of ground surface) enrolled 141 tie-stall-housed lactating Holsteins. A meta-analysis compared daily steps for exercise vs. non-exercise treatments, while generalized linear mixed models determined exercise conditions' impact on daily steps. Providing 1-hour exercise access increased daily steps by 53% (304 more steps), influenced by type of access (167 more steps outdoor vs. indoor), larger space (146 more steps) and longer outings (84 more steps). Cows spent 50-85% of exercise time idle, exploring (5-20%) and socializing (5%). Our results highlight the significant impact of 1 h daily exercise on tied cattle.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".