Marker‐assisted selection based on a multi‐trait economic index in chicken: experimental results and simulation
Bibliographic record
Abstract
A method proposed herein allows simultaneous selection for several production traits, taking into consideration their marginal economic values (i.e. the economic value of a trait's additional unit). This economic index-marker assisted selection (EI-MAS) method is based on the calculation of the predicted economic breeding value (BV), using information on DNA markers that have previously been found to be associated with relevant quantitative trait loci. Based on the proposed method, results with real birds showed that sire progeny performance was significantly correlated with expected performance (r = 0.61-0.76; P = 0.03-0.01). Simulation analysis using a computer program written specifically for this purpose suggested that the relative advantage of EI-MAS would be large for traits with low heritability values. As expected, the response to EI-MAS was higher when the map distance between the marker and the quantitative trait gene was small, and vice versa. A large number of distantly located markers, spread 10 cM apart, yielded higher response to selection than a small number of closely located markers spread 3 cM apart. Additionally, the response to EI-MAS was higher when a large number (ca.150) of progeny was used for the prediction equation.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".