Interpreting Risk from Sunscreens in the Marine Environment
Bibliographic record
Abstract
Recent and pending bans in specific jurisdictions of some organic ultraviolet (UV) filters have resulted in significant concern and controversy over the potential impacts of these contaminants in the marine environment. Organic UV filters have been quantified in the aquatic environment as contaminants in water, sediments, and the tissues of aquatic organisms. The limited available laboratory studies on the toxicity of UV filters to keystone marine species such as reef-building corals describe a wide variety of impacts, from significant acute effects to no observed effects. However, interpretation of results is complicated by differences in methodology, and exposures to single agents in vitro may not reflect the effects of longer exposure to finished sunscreens containing UV filters in combination with numerous other chemicals. Relatively short-term observations of laboratory effects thus may not translate to real-life field conditions, where organisms may be subject to the effects of long-term chronic exposure to UV filters as well as other environmental contaminants and stressors. The lack of current understanding of the full impacts of UV filters, both in the laboratory and in the environment, represents a significant challenge in interpreting the environmental risk associated with the widespread use of sunscreens.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.003 | 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".