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Record W2337114925 · doi:10.1080/10807039.2015.1115956

Estimation of dust ingestion rates in units of surface area per day using a mechanistic hand-to-mouth model

2015· article· en· W2337114925 on OpenAlexafffund
R. M. Wilson, Ian Mitchell, G. Mark Richardson

Bibliographic record

VenueHuman and Ecological Risk Assessment An International Journal · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsStantec (Canada)Calgary Laboratory ServicesCampbell Scientific (Canada)
FundersHealth CanadaU.S. Environmental Protection Agency
KeywordsIngestionEnvironmental scienceMineral dustRange (aeronautics)AerosolAtmospheric sciencesEnvironmental chemistryMeteorologyChemistryMaterials scienceGeographyGeologyComposite material

Abstract

fetched live from OpenAlex

In order to provide dust ingestion rates that can aid the interpretation of indoor dust measurements as surface loadings (i.e., units of µg/m2), dust ingestion rates have been developed for various age groups on a surface area basis. The approach incorporates a hand-to-mouth mechanistic model that was previously developed to estimate dust ingestion rates in units of mg/day. The analysis resulted in estimated mean dust intakes that range from 0.0032 m2/d (for teenagers) to 0.061 m2/d (for toddlers) at residential settings assumed to be comprised of 50% hard surfaces and 50% soft surfaces. Intake rates assuming 100% hard surfaces and 100% soft surfaces are also presented. The values provided are intended to assist the interpretation of indoor dust investigations where substance content in dust is presented as surface loadings rather than bulk dust concentrations.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.183
Threshold uncertainty score0.291

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 head0.200
GPT teacher head0.421
Teacher spread0.221 · 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 designSimulation or modeling
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

Citations11
Published2015
Admission routes2
Has abstractyes

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