An analysis of beak shape variation in two ages of domestic turkeys (Meleagris gallopavo) using landmark-based geometric morphometrics
Bibliographic record
Abstract
The objective of this study was to assess beak shape variation in domestic turkeys (Meleagris gallopavo) and determine the effects of age, sex, and beak size on beak shape variation using geometric morphometrics. Dorsal and right lateral images were taken of 2442 turkeys at 6 and 18.5 weeks of age. Landmarks were digitized in tpsDig in three analyses of the dorsal upper mandible, lateral upper mandible, and lateral lower mandible shape of each turkey at both ages. The coordinate data were then subjected to a principal components analysis (PCA), multivariate regression, and a canonical variates analysis (CVA) with a Procrustes ANOVA in MorphoJ. For the dorsal images, three principal components (PCs) showed beak shape variation ranged from long, narrow, and pointed to short, wide, and blunt upper mandibles at both ages (6 weeks: 95.36%, 18.5 weeks: 92.21%). Three PCs showed the lateral upper mandible shape variation ranged from long, wide beaks with long, curved beak tips to short, narrow beaks with short, pointed beak tips at both ages (6 weeks: 94.91%, 18.5 weeks: 94.33%). Three PCs also explained 97.80% (6 weeks) and 97.11% (18.5 weeks) of the lateral lower mandible shape variation ranging from wide and round to narrow and thin lower mandibles with superior/inferior beak tip shifts. Beak size accounted for varying proportions of the beak shape variation (0.96-54.76%; P < 0.0001) in the three analyses of each age group. For all the analyses, the CVA showed sexual dimorphism in beak shape (P < 0.0001) with female upper mandibles appearing wider and blunter dorsally with long, curved beak tips laterally. Whereas male turkey upper mandibles had a narrow, pointed dorsal appearance and short, pointed beak tips laterally. Future applications of beak shape variability could have a genetic and welfare value by incorporating beak shape variation to select for specific turkey beak phenotypes as an alternative to beak treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| 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) | 0.000 | 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 teacher head, 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".