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Record W2071839582 · doi:10.1080/10807039.2012.719381

Comparison of Two <i>In Vitro</i> Extraction Protocols for Assessing Metals’ Bioaccessibility Using Dust and Soil Reference Materials

2012· article· en· W2071839582 on OpenAlexafffundabout
Matt Dodd, Pat E. Rasmussen, Marc Chénier

Bibliographic record

VenueHuman and Ecological Risk Assessment An International Journal · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsHealth CanadaRoyal Roads University
FundersHealth Canada
KeywordsCadmiumArsenicNISTEnvironmental chemistryChromiumExtraction (chemistry)BioavailabilityEnvironmental scienceChemistryCertified reference materialsZincMetallurgyMaterials scienceDetection limitChromatographyMedicineComputer science

Abstract

fetched live from OpenAlex

ABSTRACT The bioaccessibility of arsenic, cadmium, chromium, copper, lead, nickel, and zinc in four National Institute of Standards and Technology (NIST) standard reference materials and two Canadian dust samples as determined using the Solubility/Bioavailability Research Consortium (SBRC) in vitro procedure ranged from a low of 1.8% for chromium in standard reference material NIST 2711 to a high of 95.2% for cadmium in NIST 2584. The SBRC data were compared to data generated using a modified EN-71 Toy Safety protocol conducted at two different laboratories. Results for the two extraction methods compared well with differences between the means (SBRC vs. modified EN-71) generally less than 10% for the majority of the metals. These differences between the two extraction methods were negligible compared to variability caused by (a) the inherent heterogeneity of typical house dust samples and (b) differences in ICP-MS analytical approaches employed in the different laboratories. Results indicate that the modified EN-71 method is useful and appropriate as a relatively simple, rapid, and reproducible screening test for estimating metals’ bioaccessibility in soil and dust samples.

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)0.003
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 score0.334
Threshold uncertainty score0.954

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.184
GPT teacher head0.499
Teacher spread0.315 · 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

Citations21
Published2012
Admission routes3
Has abstractyes

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