Hypothalamic-Pituitary-Adrenal Axis Activity and Robustness: Working Towards Better Breeding of Canadian Turkeys
Bibliographic record
Abstract
Animal robustness is essential in the poultry industry because of its consequences for animal health, wellbeing, and industry profitability. Strategies to improve animal robustness can include quantifying environmental sensitivity or direct selection for robustness-related traits, however, these have come with limited success. An alternative approach is to investigate the genetics of the hypothalamic-pituitary-adrenal (HPA) axis as intense selection for production traits is believed to result in a reduced HPA axis response and ability to respond to perturbations leading to physiological and behavioural problems. The glucocorticoid hormone corticosterone (CORT) is one of the main end-products of the HPA axis. Quantifying CORT in feathers (FCORT) provides an opportunity for a less invasive measure that represents average circulating level of CORT over time compared to traditional methods. Robustness-related issues are prevalent on commercial turkey farms and perturbations can impact meat quality through HPA axis activity. Therefore, the objective of this thesis was to investigate novel phenotypes (e.g., FCORT) related to HPA axis activity that could act as indicators of robustness in domestic turkeys. We developed a reliable method for quantifying FCORT in turkey feathers and characterized feather growth patterns to provide context to these measurements. Changes in energy balance are reflected in FCORT measurements which provides validation for its use as a marker of HPA axis activity. Most importantly, our findings suggest that HPA axis activity, measured via FCORT, is a heritable trait in domestic turkeys and is negatively correlated with production traits (e.g., breast yield) but positively correlated with livability traits (e.g., walking ability). Although further investigation into the relationship between FCORT and other robustness traits is required, this thesis provides exciting avenues for improving robustness, health, and wellbeing in domestic turkeys through genetic selection.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".