Model comparison and estimation of genetic parameters for body weight in commercial broilers
Bibliographic record
Abstract
Maniatis, G., Demiris, N., Kranis, A., Banos, G. and Kominakis, A. 2013. Model comparison and estimation of genetic parameters for body weight in commercial broilers. Can. J. Anim. Sci. 93: 67–77. The availability of powerful computing and advances in algorithmic efficiency allow for the consideration of increasingly complex models. Consequently, the development and application of appropriate statistical procedures for model evaluation is becoming increasingly important. This paper is concerned with the application of an alternative model determination criterion (conditional Akaike Information Criterion, cAIC) in a large dataset comprising 203 323 body weights of broilers, pertaining to 7 (BW 7 ) and 35 (BW 35 ) days of age. Seven univariate and seven bivariate models were applied. Direct genetic, maternal genetic and maternal environmental (c 2 ) effects were estimated via REML. The model evaluation criteria included conditional Akaike Information Criterion (cAIC), Bayesian Information Criterion (BIC) and the standard Akaike Information Criterion (henceforth marginal; mAIC). According to cAIC the best-fitting model included direct genetic, maternal genetic and c 2 effects. Maternal heritabilities were low (0.10 and 0.03) compared to the direct heritabilities (0.17 and 0.21), while c 2 was 0.05 and 0.04 for BW 7 and BW 35 , respectively. BIC and mAIC favoured a model that additionally included a direct-maternal genetic covariance, resulting in highly negative direct-maternal genetic correlations (−0.47 and −0.64 for BW 7 and BW 35 , respectively) and higher direct heritabilities (0.25 and 0.28 for BW 7 and BW 35 , respectively). Results suggest that cAIC can select different animal models than mAIC and BIC with different biological properties.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".