Unintended Consequences of Sunscreen
Bibliographic record
Abstract
Sunscreen is known for its importance in protecting our skin from the harmful rays that the sun emits. It has the capability to protect our skin from various diseases, such as cancer. However, the effectiveness and safety of certain sunscreen products have started becoming controversial. This is due to concerns of health risks and environmental effects that are caused due to certain toxins consisting in sunscreen. Oxybenzone (BP-3) and Octinoxate (OMC) are two main chemicals that are under review, as they have the potential to act as hormone disruptors. This topic has not been taken seriously due to lack of research. Therefore, it is essential that we start acknowledging all potential short term and long term risks associated with sunscreen, to ensure development of more effective products. This research is a secondary literature review, in which I gathered sources to explore both beneficial as well as harmful effects of sunscreen use. I was encountered with biased information as well as a limited amount of sample size, however I gained a better understanding of why it's important we further the research of this issue. Sunscreen has proven to have positive and negative impacts on society. Certain toxins in sunscreen have harmed coral reefs, affected the food pyramid, as well as affect the normal functions of hormones in our body. I would recommend spreading more public awareness about this topic, so that people are aware of some of the unintended consequences sunscreen may have.
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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".