QTL mapping of protein content and seed characteristics under water-stress conditions in sunflower
Bibliographic record
Abstract
The purpose of this study was to identify genomic regions controlling seed protein content, kernel and hull weights, and seed density in water-stress conditions in sunflower (Helianthus annuus L.). The experiments consisted of a split-plot design (water treatment and recombinant inbred lines) with three blocks in two environments (greenhouse and field). High significant variation was observed between genotypes for all traits as well as for water treatment x genotype interaction. Several specific and nonspecific QTLs were detected for all traits under well-watered and water-stress conditions. Two SSR markers, ORS671_2 and HA2714, linked to protein content were identified that have no interaction with water treatments in greenhouse conditions. We also detected the E35M60_4 marker associated with kernel weight that had no interaction with water treatments. A specific QTL for protein content was detected with important phenotypic variance (17%) under water-stress conditions. Overlapping QTLs for protein content and seed density were identified in linkage group 15. This region probably has a peliotropic effect on protein content and seed density. QTLs for protein content colocated with grain weight traits were also identified.
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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) | 0.000 | 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 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".