Bibliographic record
Abstract
In Brazil, The Brazilian Health Regulatory Agency (ANVISA) defines cosmetics as being composed of natural or synthetic substances, which are applied to the external surface of the human body. In the Resolution RDC No. 83 of 28 June 2016 there are 1373 prohibited substances in personal hygiene products, cosmetics and perfumes, based on the list of prohibited substances in the European Union document. Elements such as Cl, Ni, As, Be, Cd, Cr, I, P, Pb, Hg, Se, Zr, Co, Te, Tl and radioactive substances are present in the list. Canadian legislation establishes that toxic elements such as Pb, As, Cd, Hg and Sb should not be used as ingredients. The objective of this work was to characterize and quantify the chemical elements present in nail polishes and cosmetic clays by means of some instrumental analytical techniques. Measurements of the samples with the equipment of EDXRF of AMPTEK were performed. This equipment model MINI-X contains two detectors silicon drift, model 123SDD. Other analyzes of these samples were carried out using the Scanning Electron Microscope (SEM) together Dispersive Energy Spectrometer (DES), both Zeiss brand, EVO MA 15 model and Shimadzu XRD 7000 X-Ray Diffractometer (XRD). In addition to these techniques some clays were subjected to gamma spectrometry analysis at the Nuclear and Energy Research Institute (IPEN) to verify the radionuclides present. Obtained results indicated the presence of the following elements in the nail polishes analyzed: Mg, Al, P, Si, S, Cl, Ca, Ti, Cr, Mn, Fe, Ni, Cu, Zn, Ba and Bi. The cosmetic clays presented the Mg, Al, P, As, Se, Ba, Co, Si, S, Cl, K, Ca, Ti, Mn, Sb, Fe, Ni, Cu, Zn, Rb, Zr, Sr, Th, U, Pb and Ra elements. Some detected elements are prohibited and restrictive according to ANVISA and international legislation such as As, Cl, P, Cr, Sb, Ni, Zr and Pb. The bioaccumulation of these substances in the body can cause metabolic disorders.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".