MétaCan
Menu
Back to cohort
Record W2621370763 · doi:10.3389/frym.2017.00006

Art Materials Can Be Dangerous! How Can You Reduce Your Risk?

2017· article· en· W2621370763 on OpenAlexaff
Masood A. Shammas, Hira Shammas, Samiyah Rajput, Dildar Ahmad, Gulzar Ahmad

Bibliographic record

VenueFrontiers for Young Minds · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy Metal Exposure and Toxicity
Canadian institutionsEarl Haig Secondary School
Fundersnot available
KeywordsHealth riskInternet privacyRisk analysis (engineering)BusinessMedicineEnvironmental healthComputer science

Abstract

fetched live from OpenAlex

Art materials are used by individuals of all ages. Certain chemicals found in art materials have potential to cause health problems, especially if used over a long period of time. The risk can be greater for children, individuals with mental or physical challenges, and people with certain genetic diseases and/or unhealthy lifestyles. However, there are laws and regulations that serve to minimize the risks we take when using these materials. In a recent publication [<xref ref-type="bibr" rid="B1">1</xref>], we have proposed that the potential health risks associated with art materials can be further reduced, if the art material labels include the date when the last review of the ingredients was performed. The purpose of this article is to share our findings with younger audiences and raise awareness that: (1) we should make an effort to know the potential health risks associated with art materials we use, especially if we will use them for a long time; (2) we should use these risky materials in a responsible and careful way; and (3) we should always read labels and collect information about the ingredients in art materials and their possible effects on health.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.190
Threshold uncertainty score0.982

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.024
GPT teacher head0.256
Teacher spread0.231 · 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 designNot applicable
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

Citations0
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueFrontiers for Young MindsSame topicHeavy Metal Exposure and ToxicityFrench-language works237,207