Domestic egg-laying hens,<i>Gallus gallus domesticus</i>, do not modulate flapping flight performance in response to wing condition
Bibliographic record
Abstract
) in backyards and farms exhibit feather loss, naturally high wing loading, and limited flight capabilities. Yet, housing chickens in aviaries requires birds to navigate three-dimensional spaces to access resources. To understand the impact of feather loss on laying hens' flight capabilities, we symmetrically clipped the primary and secondary feathers before measuring wing and whole-body kinematics during descent from a 1.5 m platform. We expected birds to compensate for increased wing loading by increasing wingbeat frequency, amplitude and angular velocity. Otherwise, we expected to observe an increase in descent velocity and angle and an increase in vertical acceleration. Feather clipping had a significant effect on descent velocity, descent angle and horizontal acceleration. Half-clipped hens had lower descent velocity and angle than full-clipped hens, and unclipped hens had the highest horizontal acceleration. All hens landed with a velocity two to three times greater than in bird species that are adept fliers. Our results suggest that intact laying hens operate at the maximal power output supported by their anatomy and are at the limit of their ability to control flight trajectory.
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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) | 0.002 | 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".