43 Advancing poultry phenotyping: A novel CT-derived lung trait for turkey (Meleagris gallopavo) selection.
Bibliographic record
Abstract
Abstract Despite marked improvements in productivity, pre-slaughter mortality remains a prominent issue in turkey production, possibly due to disproportionate growth of internal organs relative to increased meat yield. Consequently, there has been a push for incorporating traits related to organ health and function into breeding programs to improve the welfare and health of turkeys. Computed tomography (CT) is becoming a popular tool to provide non-invasive cross-sectional images of internal structures which have potential to be used as novel phenotypes in livestock breeding programs. This study aims to develop CT-derived traits to assess and improve livability traits, organ traits in particular, which could be a crucial step toward optimizing both production efficiency and animal well-being. The lung trait (lung pixels) was derived by summing the number of pixels from each slice of the CT scan where lungs were detected. Lung pixel records from 5,367 purebred male turkeys from a reproduction-focused line (Line A) and 6,774 purebred male turkeys from a meat-yield focused line (Line B) were analysed. Both lung pixels and breast meat yield derived from CT (BMYct) were measured, allowing for genetic evaluation without requiring slaughter. A multi-trait model for lung pixels, BMYct, and breast weight in grams from manual cut up (BRMT) was implemented in ASREML 4.2 to estimate genetic parameters of each trait in each line. The fixed effects included body weight estimated from the CT images, age at scan, and a location factor which encompassed barn and farm. Heritability estimates for lung pixels, BMYct, and BRMT in line A were 0.29 ± 0.03, 0.55 ± 0.04, and 0.50 ± 0.04, respectively. Unfavorable genetic correlations were estimated between lung pixels and BMYct (-0.25 ± 0.07) and lung pixels and BRMT (-0.32 ± 0.07), whereas favorable genetic correlations were estimated between BMYct and BRMT (0.68 ± 0.04). Heritability estimates for lung pixels, BMYct, and BRMT in line B were 0.15 ± 0.02, 0.46 ± 0.03, and 0.33 ± 0.03, respectively. Unfavorable genetic correlations were estimated between lung pixels and BMYct (-0.15 ± 0.08) and lung pixels and BRMT (-0.19 ± 0.09), whereas favorable genetic correlations were estimated between BMYct and BRMT (0.72 ± 0.03). These results show a potential for selection on lung pixels and provide insight into the relationship between potential lung traits derived from CT and meat yield traits in two lines. By integrating CT-based selection criteria, the turkey industry could enhance sustainability, reduce mortality, and improve the overall robustness of flocks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".