Yield Ability and Yield Stability, the Effective Tools Through Selection Procedure of Classified Wheat (Triticum aestivum) Crosses
Bibliographic record
Abstract
Early generation selection in wheat breeding (Triticum aestivum), for stable high yielding genotypes, has been attempted using various parameters. The aim of the present study was the selection of genetic parameters that could be used for the assessment of crosses in the selection procedure. The experiments were conducted at the National Agricultural Research Foundation and the research farms of the Aristotle University of Thessaloniki for four growing seasons. Four out of ten crosses were chosen, based on a set of evaluation criteria estimating productivity and stability. Positive general combining ability of the parents was a prerequisite for any cross to remain in the selection procedure. The cross Oropos x Acheloos was ranked first in F1 and exhibited significant heterobeltiosis from F2 to F4 generation, producing elite F5 lines which yielded 45.05% at the best check. The ranking of the rest of the crosses in F1 remained the same in F5 generation showing that phenotyping achieved genotyping owing to isolation environment and high selection pressure. Genetic variance per cross gave a reliable estimation of the stability of the crosses through the segregating generations with Oropos x Acheloos being the less affected cross by environmental factors. It was concluded that early generation selection can successfully produce elite F5 lines, with an appropriate methodology which estimates productivity and stability, heterotic effects and the general combining ability of the parents.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".