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Record W3149805712 · doi:10.46602/jcsn.v46i2.592

Spectrophotometric determination of cadmium, chromium, lead and nickel in five selected body creams sold in Benin City, Nigeria

2021· article· en· W3149805712 on OpenAlexaboutno aff
Anthony Aiwonegbe, Moustapha Oke

Bibliographic record

VenueJournal Of Chemical Society Of Nigeria · 2021
Typearticle
Languageen
FieldChemistry
TopicHeavy Metals in Plants
Canadian institutionsnot available
Fundersnot available
KeywordsMoisturizerCadmiumChemistryAtomic absorption spectroscopyCosmeticsChromiumHealth hazardFood scienceMedicine

Abstract

fetched live from OpenAlex

The concentrations of cadmium (Cd), chromium (Cr) lead (Pb), and nickel (Ni) were evaluated in five body creams sold in different shopping malls and markets in Benin City metropolis. The samples were classified into bleaching creams, toning creams and moisturizers. The moisture content of each cream sample was determined using standard methods while the heavy content was obtained using atomic absorption spectrometry (AAS). Pb was found to have the highest concentration in bleaching cream 2 (9.70 ppm) and the lowest in moisturizer 1 (1.50 ppm). The concentration range for Cd was from 9.20 ppm in moisturizer 2 to 2.90 ppm in the toning cream. The highest concentration of Ni was found in moisturizer 1 (7.00 ppm) and the lowest was in bleaching cream 2 (1.20 ppm). Cr was not detected in the bleaching and toning creams but it was found to be 26.00ppm in moisturizer 1. The results were compared with international standards and were found to be within the limits set by the cosmetic standards of USA, Canada and WHO. The results from this study has shown that the use of these body creams exposes users to some level of concentration of these toxic metals which could constitute potential health hazard due to their ability to bio-accumulate. Therefore, there should be regular monitoring of heavy metals and other chemicals used in the manufacture of body creams.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.958

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.013
GPT teacher head0.269
Teacher spread0.256 · 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 designBench or experimental
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
Published2021
Admission routes1
Has abstractyes

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Same venueJournal Of Chemical Society Of NigeriaSame topicHeavy Metals in PlantsFrench-language works237,207