Evaluation of three recurrent selection methods in two short-season maize (Zea mays L.) synthetics
Bibliographic record
Abstract
Numerous studies of response to selection have been made in maize (' Zea mays' L.) populations. However, determining the relative effectiveness of the various selection methods is difficult because the studies have involved different selection methods, populations, selection protocols, traits, number of cycles of selection, etc. Ideally, comparison of selection methods would involve selection for the same traits by using different methods in common base populations. These data are lacking in maize and, therefore, more empirical information is needed to enable breeders to make the best choice among the several methods of recurrent selection available for the improvement of maize populations. In this study, the effects of three recurrent selection methods were examined in two short-season synthetics: Canada Guelph Stiff Stalk (CGSS) and Canada Guelph Lancaster (CGL). The primary traits selected were performance index, its two components: grain yield and grain moisture, and reduced stalk lodging. The first objective was to evaluate the genetic changes associated with selfed progeny recurrent selection (S), half-sib reciprocal recurrent selection (RRS) using an inbred tester, and the combination of both (COM). The second objective was to evaluate the relative effectiveness of each selection method through observed and realized response to selection in populations ' per se', the interpopulation cross, and their effectiveness in the production of superior inbred lines. Smith's (1983) genetic model was used to separate genetic changes due to selection from those due to genetic drift effects. None of the three methods of recurrent selection proved to be best under all circumstances. Selfed progeny selection achieved the highest response to selection for most of the directly selected traits in CGSS, while COM was the best selection procedure for CGL. Genetic changes associated with selection and realized responses to selection were distinct for each selection method within each population, and depended to a large extent on the genetic structure of the base population. Genetic drift effects hindered response to selection, thus realized response to selection adjusted for random drift effects was a better indicator than observed response of the relative effectiveness of selection procedures in the improvement of the populations 'per se ' and their crosses.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".