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Record W3163244202 · doi:10.1016/j.heliyon.2021.e07067

Alpha particle rates and heavy metal concentrations in cosmetics available in the Najaf markets

2021· article· en· W3163244202 on OpenAlexaboutno aff
Basim A. Almayahi

Bibliographic record

VenueHeliyon · 2021
Typearticle
Languageen
FieldHealth Professions
TopicRadioactivity and Radon Measurements
Canadian institutionsnot available
Fundersnot available
KeywordsCosmeticsEnvironmental chemistryHeavy metalsChemistryEnvironmental scienceOrganic chemistry

Abstract

fetched live from OpenAlex

This study focuses to measure the emission of alpha particle rates (EAPR) and heavy metal concentrations (Cd, Pb, Zn, Cr, and Co) (HMC) in some cosmetics (kohl eye, compact powder, and lipsticks) from Najaf markets using CR39 Detectors and Spectrophotometry, respectively. The EAPR range from 0.0068 to 0.0499 mBq cm−2. The mean concentration of HMC in cosmetics ranged from 0.0007 to 0.0339 ppm (Cd), 0.0002–0.0986 ppm (Pb), 0.0350–9.7786 ppm (Zn), 0.0001–0.0058 ppm (Cr), 0.0011–0.1510 ppm (Co). The concentrations of Cr and Co were below the recommended limit for skin protection (1 ppm), whereas Cd and Pb were below the Canadian recommended limit. This study concludes that the HMC and EAPR in the cosmetics were within the recommended limits. The mean HMC contents were arranged in the order: Zn > Pb > Cd > Co > Cr for lipsticks; Zn > Co > Pb > Cd > Cr for powder; Zn > Co > Pb > Cd > Cr for Kohl. A statistically significant correlation (SSC) between HMC and EAPR in cosmetics was found at the 0.05 level. All the various cosmetics brands that contained HMC levels were lower than EPA limits (1 ppm and 0.3ppm, respectively), except the Zn. The cumulative exposure to HMC in cosmetics because of prolonged use is a possible source of HMC toxicity.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.092
Threshold uncertainty score0.675

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 studies0.0000.000
Scholarly communication0.0000.000
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 head0.099
GPT teacher head0.382
Teacher spread0.284 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

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