MétaCan
Menu
Back to cohort
Record W2074196314 · doi:10.1021/es203470x

Heavy Metals in Toys and Low-Cost Jewelry: Critical Review of U.S. and Canadian Legislations and Recommendations for Testing

2012· review· en· W2074196314 on OpenAlexaffabout
Mert Güney, Gérald J. Zagury

Bibliographic record

VenueEnvironmental Science & Technology · 2012
Typereview
Languageen
FieldEnvironmental Science
TopicHeavy Metal Exposure and Toxicity
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsEuropean unionHeavy metalsLegislationBioavailabilityLegislatureEnvironmental scienceBusinessWaste managementEngineeringEnvironmental chemistryChemistryPolitical scienceMedicine

Abstract

fetched live from OpenAlex

High metal contamination in toys and low-cost jewelry is a widespread problem, and metals can become bioavailable, especially via oral pathway due to common child-specific behaviors of mouthing and pica. In this review, the U.S., Canadian, and European Union (EU) legislations on metals in toys and jewelry are evaluated. A literature review on content, bioavailability, children's exposure, and testing of metals in toys and low-cost jewelry is provided. A list of priority metals is presented, and research needs and legislative recommendations are addressed. While the U.S. and Canadian legislations put emphasis on lead exposure prevention, other toxic elements like arsenic and cadmium in toy materials are not regulated except in paint and coatings. The EU legislation is more comprehensive in terms of contaminants and scientific approach. Current toy testing procedures do not fully consider metal bioavailability. In vitro bioaccessibility tests developed and validated for toys and corresponding metal bioaccessibility data in different toy matrices are lacking. The U.S. and Canadian legislations should put more emphasis on metal bioavailability and on other metals in addition to lead. A two-step management approach with mandatory testing of toys for total metal concentrations followed by voluntary bioaccessibility testing could be implemented.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.995
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.003
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 head0.070
GPT teacher head0.337
Teacher spread0.267 · 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 designOther design
Domainnot available
GenreReview

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

Citations97
Published2012
Admission routes2
Has abstractyes

Explore more

Same venueEnvironmental Science & TechnologySame topicHeavy Metal Exposure and ToxicityFrench-language works237,207