Genetic Analysis of Maize (<i>Zea mays</i> L.) Endosperm Vitreousness and Related Hardness Traits in the Intermated B73 × Mo17 Recombinant Inbred Line Population
Bibliographic record
Abstract
Variation for maize ( Zea mays L.) kernel vitreousness is of interest in the improvement of maize for human and ruminant nutrition based on its connection with starch degradability in the rumen as well as the concentration of lysine and other essential amino acids. In this trial, 199 recombinant inbred lines (RILs) from the intermated B73 and Mo17 (IBM) population and 133 testcrosses of the IBM RIL with inbred W604S were grown in two replications in two Wisconsin locations in 2007. Ears were harvested at physiological maturity and ground kernel samples were scanned using near‐infrared spectroscopy (NIRS). Samples representing the NIRS spectral range were scored for kernel vitreousness using a horizontal light box and for kernel hardness using the Stenvert hardness test. The RIL phenotypes for hardness and vitreousness followed a normal distribution, and transgressive segregation was seen for both traits. Correlations between inbreds and respective testcrosses for hardness and vitreousness were significant and positive. The correlation between hardness and vitreousness was also positive. Quantitative trait loci (QTL) analysis of this population found 33 QTL for hardness and vitreousness with several QTL overlapping across traits. The IBM population represents a good model for the study of variation of kernel vitreousness in U.S. Corn Belt Dent germplasm and the genomic regions identified could be useful for the genetic improvement of maize varieties with enhanced nutritional composition.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".