Speciation and distribution of arsenic and localization of nutrients in rice grains
Bibliographic record
Abstract
Arsenic (As) contamination of rice grains and the generally low concentration of micronutrients in rice have been recognized as a major concern for human health. Here, we investigated the speciation and localization of As and the distribution of (micro)nutrients in rice grains because these are key factors controlling bioavailability of nutrients and contaminants. Bulk total and speciation analyses using high-pressure liquid chromatography (HPLC)-inductively coupled plasma mass spectrometry (ICP-MS) and X-ray absorption near-edge spectroscopy (XANES) was complemented by spatially resolved microspectroscopic techniques (micro-XANES, micro-X-ray fluorescence (micro-XRF) and particle induced X-ray emission (PIXE)) to investigate both speciation and distribution of As and localization of nutrients in situ. The distribution of As and micronutrients varied between the various parts of the grains (husk, bran and endosperm) and was characterized by element-specific distribution patterns. The speciation of As in bran and endosperm was dominated by As(III)-thiol complexes. The results indicate that the translocation from the maternal to filial tissues may be a bottleneck for As accumulation in the grain. Strong similarities between the distribution of iron (Fe), manganese (Mn) and phosphorus (P) and between zinc (Zn) and sulphur (S) may be indicative of complexation mechanisms in rice grains.
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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) | 0.000 | 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".