Describing the growth and molt of modern domestic turkey (<i>Meleagris gallopavo</i>) primary wing feathers
Bibliographic record
Abstract
The use of feathers as noninvasive physiological measurements of biomarkers in poultry research is expanding. Feather molting patterns and growth rates, however, are not well described in domestic poultry. These parameters could influence the measurement of these biomarkers. Therefore, the objective of this study was to describe the juvenile primary feather molting patterns and feather growth rates for domestic turkeys. The 10 primary wing feathers of 48 female turkeys were measured weekly from week 1 (0 d of age) to week 20. Feathers were manually measured, and the presence or absence of each primary feather was recorded weekly. Generalized linear mixed models were used to investigate if feather growth differed between the primary feathers. The molting of the juvenile primary feathers followed a typical descending pattern starting with P1 (5 wk of age), while P9 and P10 had not molted by the end of the study (20 wk of age). The average feather growth rate was 2.4 cm/wk, although there was a significant difference between the 10 primary feathers (P < 0.0001, 2.1 to 2.8 cm/wk). Over time, feather growth followed a pattern where the growth rate reaches a peak and then declines until the feather is molted. The results of this study provide a critical update of patterns of molting and feather growth in primary wing feathers of modern turkeys. This can have implications for the interpretation of physiological biomarkers, such as the longitudinal deposition of corticosterone, in the feathers of domestic turkeys.
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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 | 0.001 | 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".