Genetic parameters for clutch and broodiness traits in turkeys (Meleagris Gallopavo) and their relationship with body weight and egg production
Bibliographic record
Abstract
The objective of this study was to estimate phenotypic and genetic parameters for clutch and broodiness (BR) traits in turkeys and their relationship with body weight and egg production. Data on dam line hens was available and included: body weight at 18 wk of age (BW18), body weight at lighting (BWL, 29 to 33 wk), age at first egg (AFE), egg number (EN), rate of lay (RL), clutch length (CL), maximum clutch length (MCL), pause length (PL), maximum PL (MPL) and BR. BR was defined as the average number of consecutive pause days between clutches that was higher than the average PL per hen. Heritability estimates for BW18 and BWL were 0.50 and 0.53, respectively. The heritability for egg production, clutch, and pause traits varied from low (MPL = 0.15; BR = 0.15) to moderate (AFE = 0.22; EN = 0.28; RL = 0.29; CL = 0.21; MCL = 0.27; PL = 0.25). Genetic correlations were negative between body weight traits and EN (rg (BW18, EN) = -0.27; rg(BWL, EN) = -0.33) and CL (rg(BW18, CL) = -0.40; rg(BWL, CL) = -0.33). BR was negatively genetically correlated with EN (rg(BR, EN) = -0.85) and CL (rg(BR, CL) = -0.30), and positively genetically correlated with PL (rg(BR, PL) = 0.93) and AFE (rg(BR, AFE) = 0.21). EN had a positive (0.73) and a negative (-0.84) genetic correlation with CL and PL, respectively. Overall, the results of this study confirmed the negative (unfavorable) correlations between egg production and body weight. Despite unfavorable genetic and phenotypic correlations between egg production traits and those relating to BR, the inclusion of BR in a selection program through incorporation of clutch length traits and pause length traits is feasible. Integration of either clutch length traits or pause length traits in a selection index is likely to increase egg number while decreasing broodiness.
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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 | 0.001 |
| 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".