The development of laying hen locomotion in 3D space is affected by early environmental complexity and genetic strain
Bibliographic record
Abstract
Adult laying hens are increasingly housed in spatially complex systems, e.g., non-cage aviaries, where locomotion between elevated structures can be challenging for these gallinaceous birds. This study assessed the effect of early environmental complexity on spatial skills in two genetic strains. Brown (B) and white (W) feathered birds were raised in: Conventional cages with minimal complexity (Conv) or rearing aviaries with low (Low), intermediate (Mid), or high complexity (High). Birds from each housing treatment were challenged at three different time points in three different, age-appropriate vertical spatial tasks. Whites performed better than brown birds in all tests regardless of rearing environment. In chicks, test performance was predominantly explained by variation between replicates and differences in motivation for test participation. Treatment effects were seen in pubertal birds (pullets), with pullets from aviaries performing better than those from Conv. White High pullets performed better than white Mid or Low, an effect that was not found in browns. Pullets preferred to use a ramp to move downwards, but only when ramps had previously been experienced and when the ramp was not too steep. Overall, early environmental complexity affected spatial skills of laying hen pullets with stronger effects in white than brown feathered birds.
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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".