Meta-analyses of arsenic accumulation in Indica and Japonica rice grains
Bibliographic record
Abstract
Abstract Arsenic (As) is a worldwide concern because of its toxic effects on crop yield and prevalence in the food chain. Rice is consumed by half of the world’s population and is known to accumulate As. The present study reviews the available literatures on As accumulation in different subspecies of rice grains (indica, japonica and aromatic) and performs meta-analyses for grain size and texture; these data include 120 studies conducted over the last 15 years across different parts of the world. Aromatic rice varieties accumulate less As than either indica or japonica. Japonica rice varieties accumulate higher As than indica rice grains and within each subspecies polished and/or shorter rice grains accumulated significantly lower As compared to larger and/or unpolished grains. This shows that rice-based As bioaccumulation in humans could be lowered by increased use of aromatic or polished indica rice varieties, followed by the cultivation of shorter polished grains of japonica rice. These findings will be important to inform policy on rice cultivation and dietary uptake of As for a large portion of the global population.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.023 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".