Bibliographic record
Abstract
Per- and polyfluoroalkyl substances (PFAS) are widely used in cosmetics sold in the US and Canada, but most of these products do not disclose fluorinated ingredients on their labels, according to a new study ( Environ. Sci. Technol. Lett. 2021, DOI: 10.1021/acs.estlett.1c00240) . The work, led by physics professor Graham Peaslee of the University of Notre Dame, involved testing more than 200 cosmetics purchased in the two countries. More than half the products contained fluorine, suggesting the presence of PFAS, but less than 8% listed PFAS on their labels. Foundations, waterproof mascaras, and liquid lipsticks had the highest levels of total fluorine. To better understand the types of PFAS in the products, the researchers analyzed 29 of the products using targeted chromatography with mass spectrometry. They confirmed that all 29 contained PFAS. “We found new types of PFAS that hadn’t been found in prior studies,” Peaslee said at a June
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".