Monthly model for genetic evaluation of laying hens II. Random regression
Bibliographic record
Abstract
1. We investigated the use of monthly production records for genetic evaluation of laying hens, derived from a test day model with random regression in dairy cattle and compared it with other models. 2. Records of 6450 hens, daughters of 180 sires and 1335 dams, were analysed using a model with restricted maximum likelihood (REML): traits considered were monthly and cumulative egg production. Five models were studied: (1) random regression with covariates derived from the regression of Ali and Schaeffer (Canadian Journal of Animal Science, 67: 637-644, 1987) (RRMAS), (2) random regression with covariates derived from quartic polynomial (RRMP4), (3) fixed regression with covariates derived from Ali and Schaeffer (FRM), (4) multiple trait (MTM) and (5) cumulative (CM). 3. The models were compared on the basis of Spearman rank correlations of individual breeding values and sire breeding values estimated from subsets of full-sib split data. The hens (about 10% per generation) which ranked highest on their estimated breeding values from different models were compared phenotypically with their full records. 4. The estimates of heritability resulting from RRMP4 were biased upward from the estimates obtained from MTM, so this model was discarded. The heritabilities for monthly productions from RRMAS and MTM showed a similar pattern. They were high for the 1st month of production, decreased to their lowest value at about month 5 of production and increased again to the end of lay. 5. Spearman rank correlations between animal breeding values estimated by monthly models (RRMAS, FRM and MTM) were high, between 0.91 and 0.98, whereas those between estimates of monthly models and CM were lower, from 0.85 to 0.87. The correlations estimated either from intermittent months of measurements (odd vs even months) or full records were generally high, from 0.93 to 0.99. Information from odd months of production could be sufficient for cost-efficient recording schemes. The RRMAS generally had the highest correlation of sire breeding values between subsets of full-sib records, followed by MTM, RM and CM. Monthly models selected hens with higher productivity than the cumulative model. 6. In conclusion, genetic evaluation based on monthly production may be better than using cumulative production and RRMAS appeared to be the best among the models tested here.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".