Research Note: Beak morphology of infrared beak–treated laying hens and its impact on production and welfare
Bibliographic record
Abstract
Despite previous research on the impacts of beak treatment on laying hens, little information exists regarding how variation in beak morphology that can occur following beak treatment affects production, behavior, and welfare. Following infrared beak treatment (IRBT), variations in beak shape, such as a shovel beak (bottom beak longer than top), cracks (Cr), or bubbles (B) may occur if the IRBT equipment is damaged or if a quality control program is not followed at the hatchery. This study aimed to determine if variations in beak morphology post-IRBT impacted laying hen production or welfare. Infrared beak-treated Lohmann LSL-Lite hens (n = 80) were selected from a 56-wk-old flock and randomly assigned into 1 of 8 treatments: flush beak (control), shovel beak extending 0-1 mm (SB0-1), 1-2 mm (SB1-2), 2-3 mm (SB2-3), 3-4 mm (SB3-4), or >4 mm (SB > 4), Cr, or B. Hens were housed in individual cages for 4 wk and production (body weight, feed intake, egg production, and egg quality), and welfare (behavior and histology) parameters were evaluated. Consumption of different particle sizes was assessed by measuring feed particle size of refused feed. Data were analyzed as a one-way ANOVA, in a completely randomized design using PROC GLM (SAS 9.4). The results indicated that the beak morphologies examined had minimal effects on the production or welfare of the hens. Histological assessment did not show the presence of neuromas in the beak tissue, suggesting that the hens were not experiencing chronic pain from the IRBT procedure.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".