Radium levels in Brazil nuts: A review of the literature
Bibliographic record
Abstract
Brazil nuts are well known for their extraordinarily high selenium content. For this reason, they are frequently recommended as a kind of natural selenium 'supplement', particularly for certain population groups such as vegetarians and vegans in regions with low soil selenium levels. Typically, an intake of one or two Brazil nuts per day is recommended. Brazil nuts, however, also stand out from other nuts in terms of their high (albeit highly variable) radium content. The radium isotopes Ra-226 and Ra-228 emit alpha- and beta-radiation, with this type of radiation being particularly harmful when ingested. Consequently, it is important to consider radium levels in Brazil nuts before formulating recommendations for a long-term, daily intake of these nuts. To date, however, no comprehensive overview of radium levels in Brazil nuts has been published. Therefore, a literature review without time or language restrictions was conducted, including unpublished original data from Germany. The literature review (including the German data) indicated mean Ra-226 and Ra-228 levels of 49 (range: 17-205) mBq/g and 67 (range: 12-235) mBq/g, respectively. Assuming a consistent daily intake of one or two Brazil nuts, this would result in an effective dose of ~88-220 μSv/year. This level of exposure appears to be neither clearly harmful nor clearly harmless. As increased radioactivity exposure (at least at higher doses) is associated with increased cancer risk, randomised controlled trials assessing the effect of Brazil nuts on cancer risk biomarkers are needed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".