Physical Health Problems and Environmental Challenges Influence Balancing Behaviour in Laying Hens
Bibliographic record
Abstract
With rising public concern for animal welfare, many major food chains and restaurants are changing their policies, strictly buying their eggs from non-cage producers. However, with the additional space in these cage-free systems to perform natural behaviours and movements comes the risk of injury. We evaluated the ability to maintain balance in adult laying hens with health problems (footpad dermatitis, keel damage, poor wing feather cover; n = 15) using a series of environmental challenges and compared such abilities with those of healthy birds (n = 5). Environmental challenges consisted of visual and spatial constraints, created using a head mask, perch obstacles, and static and swaying perch states. We hypothesized that perch movement, environmental challenges, and diminished physical health would negatively impact perching performance demonstrated as balance (as measured by time spent on perch and by number of falls of the perch) and would require more exaggerated correctional movements. We measured perching stability whereby each bird underwent eight 30-second trials on a static and swaying perch: with and without disrupted vision (head mask), with and without space limitations (obstacles) and combinations thereof. Video recordings (600 Hz) and a three-axis accelerometer/gyroscope (100 Hz) were used to measure the number of jumps/falls, latencies to leave the perch, as well as magnitude and direction of both linear and rotational balance-correcting movements. Laying hens with and without physical health problems, in both challenged and unchallenged environments, managed to perch and remain off the ground. We attribute this capacity to our training of the birds. Environmental challenges and physical state had an effect on the use of accelerations and rotations to stabilize themselves on a perch. Birds with physical health problems performed a higher frequency of rotational corrections to keep the body centered over the perch, whereas, for both health categories, environmental challenges required more intense and variable movement corrections. Collectively, these results provide novel empirical support for the effectiveness of training, and highlight that overcrowding, visual constraints, and poor physical health all reduce perching performance.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 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".