Selenium in cereals: Insight into species of the element from total amount
Bibliographic record
Abstract
Selenium (Se) is a trace mineral micronutrient essential for human health. The diet is the main source of Se intake. Se-deficiency is associated with many diseases, and up to 1 billion people suffer from Se-deficiency worldwide. Cereals are considered a good choice for Se intake due to their daily consumption as staple foods. Much attention has been paid to the contents of Se in cereals and other foods. Se-enriched cereals are produced by biofortification. Notably, the gap between the nutritional and toxic levels of Se is fairly narrow. The chemical structures of Se compounds, rather than their total contents, contribute to the bioavailability, bioactivity, and toxicity of Se. Organic Se species show better bioavailability, higher nutritional value, and less toxicity than inorganic species. In this paper, we reviewed the total content of Se in cereals, Se speciation methods, and the biological effects of Se species on human health. Selenomethionine (SeMet) is generally the most prevalent and important Se species in cereal grains. In conclusion, Se species should be considered in addition to the total Se content when evaluating the nutritional and toxic values of foods such as cereals.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".