Human nails as a biomarker of arsenic exposure from well water in Inner Mongolia: comparing atomic fluorescence spectrometry and neutron activation analysis
Bibliographic record
Abstract
Arsenic (As) is found naturally in the geological strata within the Ba Men Region of Inner Mongolia, China. A study was conducted to compare the total As measurements from two analytical techniques: instrumental neutron activation analysis (INAA) and atomic fluorescence spectrometry (AFS), and to verify nails as an exposure biomarker in this population. In 1999, nail and water samples were collected in a pilot study. Fingernails and toenails were pooled from 32 participants and analysed for total As by both INAA and AFS. Mean nail As values were 14.8+/-2.4 and 19.4+/-2.8 microg g-1 (+/-SEM) for INAA and AFS, respectively. Results from these two methods were significantly correlated (r=0.93, p<0.0001). In 2000, a second study was conducted and INAA was used to measure total As in toenails from 314 Ba Men residents. Well water samples were collected from 121 households and analysed by AFS. A significant correlation was observed between toenail and well water As (r=0.84, p<0.0001). Based on the results, INAA was significantly correlated with AFS and proved to be a reliable measure of nail As levels. In this population, toenail samples are a useful internal As exposure biomarker from drinking water sources.
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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 | 0.000 |
| 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".