On the Integrated Usage of Atomic and Nuclear Approaches for Detecting Potentially Hazardous Elements
Bibliographic record
Abstract
In this work, we report on the usage of different atomic and nuclear approaches for detecting hazardous elements in some commercially available eye cosmetics. Recent studies showed that some eye cosmetics (e.g. eye-liners like kohl) might have hazardous and toxic elements in its elemental composition, which would harmfully impact on the environment and health of its users. In order to obtain accurate information on the elemental content of some natural and synthetic eye-liners that are commercially available in the Egyptian markets and pharmacies, we have applied Energy Dispersive X-ray (EDX), Atomic Absorption Mass Spectroscopy (AA-MS), Elemental Analyzer (EA), and Neutron Activation Analysis (NAA). Heavy and toxic elements, as well as short and long lived radionuclides concentration values were identified in the studied samples. The results indicate that among the three studied samples, the highest lead containing sample is the natural unprocessed one of African source; while the most abundant element in the synthetic samples made in France and USA is Carbon. The present study raises a concern about the medical and environmental implications of using eye-liners, and emphasizes the vital role played by atomic and nuclear approaches in detecting hazardous elements in such commercially available products.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".