Partitioning of genetic values associated with identified genotype and residual genotype
Bibliographic record
Abstract
ABSTRACT A simplified partition procedure was developed to partition the genetic value associated with the identified genotype (a combined genotype of all quantitative trait loci (QTL) identified) and residual genotype. The simplified partition procedure does not require the construction of mixed model equations for both identified and residual genotypes, and therefore drastically reduces the computing requirements as compared with the direct partition procedure. Both the simplified and the direct partition procedures were shown to be equivalent theoretically and experimentally. The simplified partition procedure also applies to the partitioning of other random effects such as the partition of sire effect into two components (constant and interaction sire effects) without actually solving the mixed model equations of the partitioned sire model. The relative contribution of the identified loci and the residual genotypes to the genetic value of a trait depends on their correlation (ρqr) and the ratio of their genetic variances (σ2q/σ2r). Identifying more QTL or increasing QTL variance would add to the contribution of identified QTL to the total genetic value of a quantitative trait. However, the additional contribution of identifying each extra QTL increases at a decreasing rate when the correlation between identified and residual genotypes is positive, but at an increasing rate when the correlation is negative. An effective QTL‐assisted selection program should consider both direct and associated effects of the identified loci.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".