A Combined Analysis in Complementary Progeny Tests: Effects on breeding value accuracies
Bibliographic record
Abstract
Abstract Complementary progeny tests allow for simultaneously ranking parents for their general combining ability (GCA) and within-family forward selection. To do this, progeny tests are established with different types of genetic entries (i.e., half-sib and full-sib seedlings, respectively), and different experimental designs. This study proposes a combined analysis of the GCA and full-sib (FS) tests using the mixed model approach to predict simultaneously the breeding values of grandparents, parents, full-sib families and offspring on the same scale. Moreover, a first order autoregressive spatial mixed model for the GCA tests was also implemented in the combined analysis. Our empirical study in coastal Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) shows that additional information provided from relatives and the overlap genetic entry among GCA and FS tests via the proposed combined analysis, improves the accuracies of breeding values compared to the non-combined analysis. The improvements in the accuracies of breeding values for backward and forward selections were generally modest. Spatial and combined analyses gave slightly better results than the non-spatial combined model.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".