Identification of quantitative trait loci for cadmium accumulation and distribution in rice (<i>Oryza sativa</i>)
Bibliographic record
Abstract
Cadmium (Cd) poses a serious risk to human health due to its biological concentration through the food chain. To date, information on genetic and molecular mechanisms of Cd accumulation and distribution in rice remains to be elucidated. We developed an independent F7 RIL population derived from a cross between two japonica cultivars with contrasting Cd levels, 'Suwon490' and 'SNU-SG1', for QTLs identification of Cd accumulation and distribution. 'Suwon490' accumulated five times higher Cd in grain than 'SNU-SG1'. Large genotypic variations in Cd accumulation (17-fold) and concentration (12-fold) in grain were found among RILs. Significant positive correlations between Cd accumulation in grain with shoot Cd accumulation and shoot to grain distribution ratio of Cd signify that both shoot Cd accumulation and shoot to grain Cd distribution regulate Cd accumulation in japonica rice grain. A total of five main effect QTLs (scc10 for shoot Cd accumulation; gcc3, gcc9, gcc11 for grain Cd accumulation; and sgr5 for shoot to grain distribution ratio) were detected in chromosomes 10, 3, 9, 11, and 5, respectively. Of these, the novel potential QTL sgr5 has the strongest effect on shoot to grain Cd distribution. In addition, two digenic epistatic interaction QTLs were identified, suggesting the substantial contribution of nonallelic genes in genetic control of these Cd-related traits.
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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".