Concentrations and exposure risks of some metals in facial cosmetics in Nigeria
Bibliographic record
Abstract
The concentrations of nine metals (Cd, Pb, Ni, Cr, Co, Cu, Fe, Mn and Zn) were determined in lip sticks, lip glosses, lip balms, eye pencils, eyeliners, eye shadows, blushes, mascaras and face powders. The study was aimed at providing information on the risk associated with human exposure to metals in these facial cosmetic products. The concentrations of metals in the samples were measured by atomic absorption spectrometry after digestion with a mixture of nitric acid, hydrochloric acid and hydrogen peroxide. The mean concentrations of metals in these facial cosmetics ranged from 3.1 to 8.4 μg g −1 Cd, 12–240 μg g −1 Pb, 9.1–44 μg g −1 Cr, 18–288 μg g −1 Ni, 1.6–80 μg g −1 Cu, 7.9–17 μg g −1 Co, 2.3–28 mg g −1 Fe, 12–230 μg g −1 Mn, and from 18 to 320 μg g −1 Zn. The concentrations of Ni, Cr and Co were above the suggested safe limit of 1 μg g −1 for skin protection, while Cd and Pb were above the Canadian specified limits. The systemic exposure dosage (SED) values for these metals obtained from the use of these facial cosmetic products were below their respective provisional tolerable daily intake (PTDI)/or recommended daily intake (RDI) values. The margin of safety values obtained were greater than 100 which indicated that the concentrations of the metals investigated in these facial cosmetics do not present considerable risk to the users except in the case of face powders.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".