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Record W4206975517 · doi:10.3390/geosciences12020052

Determination of Total Mercury and Carbon in a National Baseline Study of Urban House Dust

2022· article· en· W4206975517 on OpenAlexafffundabout
Christine Levesque, Pat E. Rasmussen

Bibliographic record

VenueGeosciences · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsUniversity of OttawaHealth Canada
FundersHealth Canada
KeywordsMercury (programming language)Environmental chemistryEnvironmental scienceTotal organic carbonAnimal scienceParticulatesChemistry

Abstract

fetched live from OpenAlex

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.267

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.019
GPT teacher head0.263
Teacher spread0.244 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations6
Published2022
Admission routes3
Has abstractyes

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