Effect of Recurrent Selection on Combining Ability in Maize Breeding Populations
Bibliographic record
Abstract
Recurrent selection (RS) is a population improvement method that increases the frequency of favorable alleles while maintaining genetic variation in breeding populations. Twelve University of Guelph RS maize (Zea mays L.) populations selected via reciprocal recurrent selection (RRS), selfed‐progeny recurrent selection (S), or a method combining RRS and S (COM), were assessed for changes in the genetic structure of grain yield, grain moisture, and broken stalks, and two associated selection indices. Partitioning of the entry sums of squares from diallel matings of the original (C0) and advanced (CA) cycle populations using Gardner and Eberhart's Analysis II and Analysis III indicated genetic improvement occurred for the per se and cross performance of most populations. Accompanying the favorable changes in population performance were less favorable shifts from predominantly additive genetic effects in C0 to greater nonadditive genetic effects in CA This shift did not substantially change the general combining ability estimates (gi) of most populations. However, for grain yield, the underlying components of gi effects were altered in their relative importance. General combining ability (GCA) effects in the C0 were caused primarily by the population per se effects (vi), while in CA the GCA effects were caused predominately by parental heterotic effects (hi).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".