Relative contribution of rice and fish consumption to bioaccessibility-corrected health risks for urban residents in eastern China
Bibliographic record
Abstract
There are global concerns about dietary exposure to metal(loid)s in foods. However, little is known about the relative contribution of rice versus fish to multiple metal(loid) exposure for the general population, especially in Asia where rice and fish are major food sources. We compared relative contributions of rice and fish consumption to multi-metal(loid) exposure on the city-scale (Nanjing) and province-scale in China. The effects of ingestion rate, metal(loid) level, and bioaccessibility were examined to calculate modeled risk from Cu, Zn, total As (TAs), inorganic As (iAs), Se, Cd, Pb, and methylmercury (MeHg). Metal(loid) levels in rice and fish samples collected from Nanjing City were generally low, except iAs. Metal(loid) bioaccessibilities in fish were higher than those in rice, except Se. Calculated carcinogenic risks induced by iAs intake (indicated by increased lifetime cancer risk, ILCR) were above the acceptable level (1 0 −4 ) in Nanjing City (median: 3 × 10 −4 for female and 4 × 10 −4 for male) and nine provinces (1.4 × 10 −4 to 5.9 × 10 −4 ) in China. Rice consumption accounted for 85.0% to 99.8% of carcinogenic risk. The non-carcinogenic hazard quotients (HQ) for single metals and hazard index (HI) for multi-metal exposure were < 1 in all cases, indicating of their slight non-carcinogen health effects associated. In Guangdong and Jiangsu provinces, results showed that rice and fish intake contributed similarly to the HI (i.e., 42.6% vs 57.4% in Guangdong and 54.6% vs 45.4% in Jiangsu). Sensitivity analysis indicated that carcinogenic risk was most sensitive to rice ingestion rate and rice iAs levels, while non-carcinogenic hazard (i.e., HQ and HI) was most sensitive to ingestion rate of fish and rice, and Cu concentration in rice. Our results suggest that rice is more important than fish for human dietary metal(loid) exposure risk in China, and carcinogenic risk from iAs exposure in rice requires particular attention.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".