Determination of Total Mercury and Carbon in a National Baseline Study of Urban House Dust
Bibliographic record
Abstract
Mercury (Hg) is one of the top ten chemicals of concern for public health, according to the World Health Organization. This study investigates Hg concentrations in house dust collected from urban single family homes, to better understand typical indoor residential exposures. Using direct solid sample analysis, total Hg and carbon (TC) were determined in the <80 µm fraction of settled dust samples collected under the Canadian House Dust Study. Hg concentrations displayed a log-normal distribution with median/geomean of 0.68/0.70 mg/kg (n = 995). A small subset (<1%) of homes exhibited anomalously high dust Hg concentrations (>9.0 mg/kg). A comparison of Hg concentrations in fresh dust and household vacuum dust collected from the same homes indicated no significant difference in the two sampling methods. Total carbon concentrations displayed a median/geomean of 29.3/28.5% (n = 1011). A significant correlation between total Hg and TC in house dust (p < 0.00001) reflects the association between Hg and organic carbon previously observed in soil and sediments. The results of this study indicate a 10-fold enrichment in house dust compared with the average background concentrations reported for soil and sediments (0.07 mg/kg). The observed enrichment is attributable to Hg emissions from indoor sources and/or Hg carried home from occupational 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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".