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Record W3113423959 · doi:10.47372/ejua-ba.2020.3.34

INVESTIGATION OF MERCURY AND TITANIUM CONTENTS IN SKIN WHITENING CREAMS COMMONLY USED IN YEMEN BY ICP-MS

2020· article· en· W3113423959 on OpenAlexaboutno aff
Shaif Mohamed Kasem Saleh, Omeima A. Abdul Ghani, Abdulaziz N. Amro, Thamer S. Alraddadi

Bibliographic record

VenueElectronic Journal of University of Aden for Basic and Applied Sciences · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicCultural Heritage Materials Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsMercury (programming language)CosmeticsChemistryEnvironmental chemistry

Abstract

fetched live from OpenAlex

In this study, ten (10) samples of skin-whitening creams were analyzed for determination of mercury and titanium. The samples were collected from various retail shops, pharmacies and beauty aid stores in the local market of Yemen. Levels of mercury and titanium in creams were determined using Inductive Coupled Plasma with Mass Spectrometry (ICP-MS). The concentration of mercury in the creams ranged from below 0.0167 to 47151 μg/g and that of titanium ranged from below 0.0083 to 59.442 μg/g. Fifty percent (50%) of creams samples for mercury had concentrations more than Maximum Permissible Limits by specifications of the US Food and Drug Administration’s, (USFDA), German and Canada (Maximum Acceptable Limit of 1μg/g). The use of such creams may lead to health hazards. Therefore, it is recommended that all skin whitening creams should be checked for mercury levels and other toxic metals before marketing. Further research to better understand the sources of mercury and other toxic metals in whitening creams and other cosmetic products is recommended.

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)0.000
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.582
Threshold uncertainty score0.178

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.032
GPT teacher head0.197
Teacher spread0.165 · 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 designQualitative
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

Citations1
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueElectronic Journal of University of Aden for Basic and Applied SciencesSame topicCultural Heritage Materials AnalysisFrench-language works237,207