Comparison of zinc, iron and selenium accumulation between synthetic hexaploid wheat and its tetraploid and diploid parents
Bibliographic record
Abstract
Synthetic hexaploid wheat (SHW; genome AABBDD) was derived from a cross of Triticum turgidum L. (BBAA) with Aegilops tauschii Coss. (DD). Systematic investigations of the effects of allohexaploidization on zinc (Zn), iron (Fe), and selenium (Se) accumulation are still lacking. Here, the Fe, Zn, and Se concentrations in SHW lines and their tetraploid and diploid parents were compared in a hydroponic culture experiment. Triticum turgidum showed larger genotypic variation of Zn and Fe concentrations than SHW and A. tauschii. The Se concentration of A. tauschii was significantly higher while its Zn and Fe concentrations were lower than in tetraploid wheat and SHW. Although the Zn and Fe concentrations of some SHW lines increased approximately 20%, no SHW line showed a higher Se concentration than its high-value parent. However, because of the generally increased biomass in SHW lines, the total Se content per plant of most SHW lines was higher than their high-value parents. These new SHWs with high micronutrient content provide novel resources for biofortification breeding. Additionally, the micronutrient concentrations in some SHW lines were not consistent with those in their parents, indicating heterosis or non-additive effects that might result from interaction among the A, B, and D genomes after allopolyploidization.
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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) | 0.001 | 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".