Arsenic in Rice: I. Estimating Normal Levels of Total Arsenic in Rice Grain
Bibliographic record
Abstract
High levels of arsenic (As) in rice grain are a potential concern for human health. Variability in total As in rice was evaluated using 204 commercial rice samples purchased mostly in retail stores in upstate New York and supplemented with samples from Canada, France, Venezuela, and other countries. Total As concentration in rice varied from 0.005 to 0.710 mg kg(-1). We combined our data set with literature values to derive a global "normal" range of 0.08-0.20 mg kg(-1) for As concentration in rice. The mean As concentrations for rice from the U.S. and Europe (both 0.198 mg kg(-1)) were statistically similar and significantly higher than rice from Asia (0.07 mg kg(-1)). Using two large data sets from Bangladesh, we showed that As contaminated irrigation water, but not soil, led to increased grain As concentration. Wide variability found in U.S. rice grain was primarily influenced by region of growth rather than commercial type, with rice grown in Texas and Arkansas having significantly higher mean As concentrations than that from California (0.258 and 0.190 versus 0.133 mg kg(-1)). Rice from one Texas distributor was especially high, with 75% of the samples above the global "normal" range, suggesting production in an As contaminated environment.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".