Genotypic Selection for Soybean Earliness
Bibliographic record
Abstract
Obtaining early-maturing soybean cultivars with high yield performance has long been the focus of breeding programs. Obtaining the estimates of genetic and phenotypic parameters can assist breeders at selecting the superior genotypes. Therefore, the aim was to estimate the genetic and phenotypic parameters of progenies throughout the recurrent selection for early maturity in soybeans and to select progenies with superior agronomic traits. S0:1 progenies were evaluated in one site during the 2015/2016 harvest using a 12 × 12 lattice design, with one 2-meter row plot with two replicates. The S0:2 progenies were evaluated during the 2016/2017 harvest using an 8 × 8 lattice design, with one 3-meter row plot with three replicates. The S0:3 progenies were evaluated during the 2017/2018 harvest using a 5 × 5 lattice design, with two 3-meter rows plots with three replicates. Both S0:2 and S0:3 progenies were evaluated in three different sites. The days to flowering, full maturity, first pod insertion height, plant height, lodging index and grain yield traits was evaluated. The data were analyzed using a mixed model approach. The genetic and phenotypic parameters, expected gain with selection, realized heritability, correlated response and the frequency distributions of the adjusted means were estimated. The estimates of the variance components have evidenced variability among the progenies, enabling the selection of superior genotypes. All the evaluated progenies showed good agronomic performance, combining early maturity and productive performance. When early-maturing progenies were selected, there was a reduction in days to flowering, plant height, first pod insertion height, lodging and yield.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".