Extending wipe sampling methodologies to elements other than lead
Bibliographic record
Abstract
Wipe sampling is the USA regulatory protocol for determination of "dust lead loadings" in residential environments. Few studies have applied the wipe sampling method to metals other than lead (Pb) for the purpose of residential exposure assessments. This study was undertaken to develop and expand the wipe method for quantifying additional metal(loid)s including arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), nickel (Ni), and antimony (Sb); and to provide information on typical background loadings for these metals in urban Canadian homes. A total of 932 wipe samples, 220 field blanks, and 220 duplicate wipes were collected from 222 homes located in three cities in Ontario, Canada using the ASTM 1728 standard. The wipes were digested using a modified version of the ASTM 1644 standard for Pb, which prescribes a hot nitric acid/hydrogen peroxide digestion. The key modification was the addition of hydrofluoric acid to improve recoveries of the target elements, and determination using Inductively Coupled Plasma-Mass Spectrometry (ICP-MS). Generally, a large proportion of the results fell below the limits of detection (LOD) and quantification (LOQ). To distinguish "elevated" metal loadings from loadings characterized as "urban background", an upper background threshold for each element was derived using a normality (Q-Q) plot. LOQ was determined to be the appropriate minimum threshold based on quality assurance criteria. It is concluded that wipes are a useful sampling option to investigate multi-element loadings in residential environments.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".