Levels of heavy metals in selected facial cosmetics marketed in Dar es Salaam, Tanzania
Bibliographic record
Abstract
The aim of this study was to determine the levels of heavy metals: lead, cadmium, copper, zinc, arsenic and mercury in facial cosmetics (lipstick, lip glossy, facial powder, foundation, eyeliner, eye shadow and mascara) which were purchased randomly in Dar es Salaam, Tanzania. The levels of lead, cadmium, copper and zinc were determined using Atomic Absorption Spectrometry (AAS). The levels of arsenic were determined using Hydride Generation Atomic Absorption Spectrometry (HGAAS), and levels of mercury were determined using Cold Vapour Atomic Absorption Spectrometry. Prior to determination of the concentration of heavy metals, the samples were acid digested. The average order of concentration of heavy metals in the sample was found to be zinc > lead > cadmium > copper >arsenic > mercury (foundation), zinc > cadmium > lead > arsenic > copper > mercury (powder), copper > lead > cadmium > zinc > arsenic > mercury > (eye shadows), zinc > copper > lead > cadmium > mercury > arsenic (eyeliners), zinc > cadmium > mercury > copper > lead >arsenic (mascaras), zinc > lead > cadmium > arsenic > copper > mercury (lipsticks), lead > cadmium > zinc > copper >arsenic > mercury (lip glossy). The observed higher percentage concentrations of heavy metals beyond limits of Canadian standards for cosmetics were as follows: lead 62.79%, cadmium 16.28%, arsenic 34.88% and mercury 6.98%.
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 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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".