Predicting stress sensitivity of laying hens by identifying genetic, incubation and rearing factors
Bibliographic record
Abstract
Genetic differences exist in performance and adaptive capacity of chickens, for instance between brown and white strains. Effects of genetic, animal related and environmental factors on technical performance, health, and welfare of laying hens were determined during incubation, rearing, and laying. Hatchability was affected by strain, breeder age, egg weight uniformity, length of egg storage and season, but egg weight loss did not have a significant effect on hatchability. Predicted hatchability of brown strains was higher than that of the white strains (on average Δ = 2.02%). During the (maternal) laying phase, clutch size (CS) was included as a factor determining rearing success, while in the rearing phase of the offspring, three rearing traits (first week mortality (FWM), rearing abnormalities (RA), natural death (ND)) were included. RS was defined as the percentage of animals that survived to the laying barn relative to the number of chicks that hatched from a batch. Genetic parameters for each trait were estimated, using a Linear Mixed Model. Additionally, a Genome Wide Association Study (GWAS) was done to scan the genomes of the breeders to reveal Single Nucleotide Polymorphisms (SNPs) associated with these traits. GWAS revealed 12 different SNPs having a significant effect on RS. To further confirm the existence of these SNPs, Bayesian network analysis (BN) was used to analyze these traits and the SNPs. The results of the BN disclosed 28 SNPs associated to the traits, ten distinct SNPs each were associated with CS and RA, a single SNP with ND and seven different SNPS with FWM. It can be concluded that animal related and environmental factors are important in predicting hatchability in laying hens while CS, FWM, RA and ND are all relevant traits in investigating RS.
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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) | 0.001 | 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".