Determining the impact of active ingredients on sunscreen UV resistance
Bibliographic record
Abstract
In our continued efforts to eradicate skin cancer and other photodamaging effects caused by ultraviolet (UV) radiation, sunscreening agents have become our primary defense. However, despite the undeniable benefits of sunscreen, concerns have been raised due to the carcinogenic properties of several of their common active ingredients. Additionally, previous research has shown the unreliability of using sun protection factor (SPF) as a measure of sunscreen photoprotective ability. Given these considerations, this study set out to determine the impact different active ingredients would have on sunscreen UV resistance. It was hypothesized that the active ingredients’ UV absorbance spectrum would provide a better predictor of sunscreen photoprotection. In this report, three different sunscreens were chosen – one with titanium dioxide, one with benzophenone derivatives, and one with salicylates – to provide a broad range of tested sunscreens. Due to their similarity with human DNA, Saccharomyces cerevisiae was cultured and used as a model system for these experiments. By comparing the percentage decrease of live cells protected with sunscreen relative to a control (i.e. no exposure to UV radiation), it was found that a titanium dioxide-based sunscreen was the most effective. Notably, all three sunscreens tested had an SPF 50, yet yielded different results in photoprotective ability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".