Arsenic exposure and seafood intake in Korean adults
Bibliographic record
Abstract
Arsenic (As) is widely distributed in the environment, and humans can be exposed to As from various sources such as air, water, soil, and food. This study was performed to evaluate the As exposure levels in Korean adults by measuring total As in urine and its relation with the consumption of seafood, a favorite food in Korea. A total of 2077 adults were the study subjects; they ranged in age from 19 to 83, and they were recruited by probability sampling stratified by area, sex, and age. None of the subjects had been exposed to As occupationally. We collected information about the demographic characteristics, lifestyles, and food consumption of study subjects using a questionnaire and followed urine sampling. Diet was assessed in individual interviews using the 24-h recall method. Total As in urine was analyzed using inductively coupled plasma mass spectrometry (PerkinElmer NEXION 300S; Concord, Ontario, Canada). The geometric mean concentration of total As in urine was observed to be 97.6 µg/L and was higher in males (103.9 µg/L) than in females (93.0 µg/L). Total As levels in urine were affected by sex, age, seafood intake, and geographic location. In this study, total As in urine was positively correlated with fish and shellfish consumption, and was mainly determined by As intake through fish and shellfish/grains/flavors. These findings suggest that seafood consumption might be a major contributor to urinary As levels in Korean adults.
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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 | 0.001 |
| 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) | 0.001 | 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".