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Record W1506496465

Wipe Sampling Methodologies to Assess Exposures to Lead and Cadmium in Urban Canadian Homes

2010· article· en· W1506496465 on OpenAlexfundaboutno aff
Lauren T. McDonald, Pat E. Rasmussen, Marc Chénier, Christine Levesque

Bibliographic record

VenueScholarWorks@UMassAmherst (University of Massachusetts Amherst) · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy Metal Exposure and Toxicity
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaHealth Canada
KeywordsCadmiumSampling (signal processing)Environmental scienceEnvironmental chemistryChemistryEngineering
DOInot available

Abstract

fetched live from OpenAlex

Wipe sampling methods are widely used to quantify lead (Pb) loadings inside homes. In the present study we expand the wipe sampling method to investigate other elements in addition to Pb, namely cadmium (Cd) and the soil tracer yttrium (Y).\nFollowing the ASTM 1728 sampling protocol, 1372 wipe samples (including field blanks and duplicates) were collected from 222 homes using Ghost Wipes™. All wipe samples were digested according to a modified version of the ASTM 1644 digestion protocol in which hydrofluoric acid was added to enhance extraction efficiency, and analyzed using ICP-MS. Recoveries assessed using NIST certified reference materials were 93±6% for Pb and 88±14% for Cd (n=66).\nResults indicated that 43% of Pb and 23% of Cd samples were below LOD (932 ng m-2 and 125 ng m-2 respectively). Threshold values of 125 µg m-2 for Pb and 4.4 µg m-2 for Cd, identified using Q-Q plots, were used to distinguish “elevated” loading values from "background" loading values. Indoor sources and tracked-in soil were identified as potential contributors to elevated loading values. Spearman ranking indicated strong spatial associations amongst the metals. The study shows that wipe sampling provides useful information on room-to-room variability of metals, shedding light on possible sources of metals in residential environments.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.474
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.057
GPT teacher head0.277
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2010
Admission routes2
Has abstractyes

Explore more

Same venueScholarWorks@UMassAmherst (University of Massachusetts Amherst)Same topicHeavy Metal Exposure and ToxicityFrench-language works237,207