Rearing laying hens: Environmental complexity and genetic strain affect pullet but not chick performance in a T-maze learning task
Bibliographic record
Abstract
Laying hens require well-developed cognitive spatial skills to find and retain the location of resources in a complex housing environment. Spatial skills develop early with a sensitive period in the first few weeks of life. We aimed to test whether the performance of laying hen chicks/pullets in a two-dimensional spatial learning task was affected by the degree of spatial complexity experienced during the first few weeks of life. We hypothesised that 1) increased spatial complexity during early life would improve the performance of laying hen chicks/pullets in a T-maze, 2) chick/pullet performance in the task would differ between genetic strains, and 3) genetic strain and rearing environment would have an interactive effect on performance in the task. Four flocks of brown and white feathered chicks were raised in conventional cages (Conv), and rearing aviaries with low (Low), intermediate (Mid), or high complexity (High) during the first six weeks. Chicks were tested during week four in a simple spatial task (T-maze) and given five trials to locate a mirror (social reward). In this test, the number of correct choices and the latency to choose were recorded. Pullets of flocks three and four were habituated, trained, and tested in a more extended spatial task (T-maze) during weeks 13 and 14. In the testing phase, they were given a maximum of 15 trials to learn the location of a reward (food plus the option to escape). The learning criterion was defined as four correct choices within five consecutive trials. Test performance of chicks, while overall poor, was mostly affected by flock (χ2= 19, p= 0.0003). In support of our hypotheses, pullets from High and Mid demonstrated improved learning performances (χ2= 12.98, p= 0.005). As expected, strain differences were found in both age groups, with white chicks being faster in choosing a side (χ2= 4.11, p= 0.04), and white pullets being quicker to reach the learning criterion than their brown-feathered counterparts (pullets: χ2= 28.44, p<0.0001). There were no consistent genotype by environment interactions. In conclusion, laying hen spatial skills appeared to be sensitive to the degree of early life complexity as well as genetic strain.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".