The effects of four types of enrichment on feather-pecking behaviour in laying hens housed in barren environments
Bibliographic record
Abstract
Abstract Severe feather pecking, a potentially stereotypic behaviour in chickens (Gallus gallus) , can be reduced by providing enrichment. However, there is little comparative information available on the effectiveness of different types of enrichment. Providing forages to birds is likely to decrease feather-pecking behaviour the most, as it is generally thought that feather pecking stems from re-directed foraging motivation. Yet, other types of enrichment, such as dustbaths and novel objects, have also been shown to reduce feather pecking. In order to develop a practical and effective enrichment, these different possibilities must be examined. Using a Latin Square Design, 14-week old birds were given each of four treatments: i) forages; ii) novel objects; iii) dustbaths; or iv) no enrichment. The amount of feather-pecking behaviour and the number of pecks to the enrichments were recorded. Results showed feather pecking to be highest when no enrichment was present and lowest when the forages were present, with the other two enrichments intermediate. This was despite the fact that the numbers of pecks birds gave to the forages and dustbaths were not significantly different, suggesting that they were similarly used. Thus, we suggest here that forage enrichments are most effective at alleviating feather pecking at least in the short term and attempts should be made to develop poultry housing that allows for natural foraging behaviour. Following this, providing any kind of enrichment will increase bird welfare and is therefore still beneficial.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".