Genetic parameter estimation and evaluation of Duroc boars for feed efficiency and component traits
Bibliographic record
Abstract
MacNeil, M. D. and Kemp, R. A. 2015. Genetic parameter estimation and evaluation of Duroc boars for feed efficiency and component traits. Can. J. Anim. Sci. 95: 155–159. The objective of this research was to produce a genetic evaluation for traits related to feed efficiency of Duroc boars. Meeting this objective required partitioning phenotypic (co)variance into additive genetic and environmental components for feed intake and traits indicative of growth and body composition. Boars (N=3291) were housed in group pens of 22 to 24 animals with two electronic feeders per pen and feed intake was recorded for 8 to 14 wk. Body weight was recorded for each boar at the start and end of test, at approximately 100 kg and at up to three times during the test. The pedigree used contained sire and dam of each boar with at least one recorded phenotype (N=4651) and their maternal and paternal grandsires. Variance components were estimated by restricted maximum likelihood for animal models in a series of uni-variate and bi-variate analyses. Two multiple trait genetic evaluations were conducted to predict estimated breeding value for feed intake using animal models. The first evaluation included feed intake (h2=0.33±0.05), age at 100 kg (h2=0.31±0.04), and subcutaneous fat depth (h2=0.47±0.05). The second genetic evaluation included feed intake, average daily gain (h2=0.27±0.04), mid-test weight (h2=0.33±0.05), and subcutaneous fat depth. Genetic correlations of feed intake with age at 100 kg and fat depth were –0.80±0.05 and 0.57±0.08, respectively. Estimated breeding values for measures of feed efficiency (residual feed intake and residual gain) were calculated from the results of the second analysis and the associated additive genetic (co)variance components.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".