Genetic Architecture Underlying Kernel Quality in Food‐Grade Maize
Bibliographic record
Abstract
ABSTRACT Kernel quality of food‐grade maize (Zea mays L.) impacts the quality of the final product and is related to dry matter loss during the alkaline cooking process. Kernel size, density, and endosperm hardness are the main kernel characteristics that determine kernel quality. Insight into the genetics governing food‐grade kernel quality parameters and the influence of environment on them was gained through a series of genetic experiments. We initially characterized kernel quality in a breeding cross involving two white food‐grade maize inbred lines, SD79 and SD80. Two hundred forty F2:3–derived lines, the two parental inbred lines, and the F1 were grown in replicated trials at one location. Test weight, 50‐kernel weight, and percentage of thin kernels were measured and then compared with data collected from the Stenvert hardness test assay. Using these kernel quality parameters five high‐quality (H) and five low‐quality (L) lines were identified. The five H lines and five L lines were subsequently used in a H × H, L × L, and H × L mating scheme to examine the genetic effects governing the main kernel quality parameters (test weight, kernel size, and kernel weight). The 45 hybrids from the combination of the two partial diallels and the NC Design II mating scheme were grown in replicated trials at three locations. In general, the inheritance of test weight, kernel weight, and kernel size is influenced primarily by additive genetic effects.
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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 | 0.001 | 0.001 |
| 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".