Arsenic exposure and seafood intake in Korean adults
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.007 | 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 it