Robustness of QTLs across germplasm pools using a model quantitative trait
Bibliographic record
Abstract
Knowledge of the inheritance of C-glycosyl flavone synthesis in maize (Zea mays L.) silk tissues has been acquired through detailed genetic studies involving primarily germplasm from the Corn Belt Dent race. To test the robustness of this genetic knowledge, we examined C-glycosyl flavone synthesis in a genetically distinct germplasm pool, popcorn. C-glycosyl flavone profiles and levels and the involvement of three specific genes/quantitative trait loci (p, pr1, and sm1) in C-glycosyl flavone synthesis were examined in popcorn germplasm representing at least two races and various diverse geographic regions. Twenty-four inbred lines and 23 hybrids involving these inbred lines and inbred line R17 were characterized for their flavone profiles and levels in silk tissues. Two F2 mapping populations were constructed to examine the involvement of p, pr1, and sm1 on C-glycosyl flavone synthesis. C-glycosyl flavone levels threefold higher than previously reported in Corn Dent Belt materials and a novel class of compounds were discovered. The gene action of sm1 was different, the functional p allele was not always dominant, and pr1 did not affect mays in synthesis. Based on this rather simplistic "model" quantitative trait, it appears that caution should be exercised when attempting to apply quantitative trait locus knowledge accumulated in one germplasm base to a germplasm base that is known to be distinctly unique.
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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 | 0.004 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".