Bibliographic record
Abstract
Consumer products are important sources of exposure to harmful chemicals.Product composition is of en a mystery to users, however, due to gaps in the laws governing ingredient disclosure.A unique data set that the California Air Resources Board (CARB) uses to determine how volatile organic chemicals (VOCs) from consumer products af ect smog formation holds a partial solution.By analyzing CARB data on VOCs in consumer products, researchers identif ed and quantif ed emissions of volatile chemicals regulated under the California Safe Drinking Water and Toxic Enforcement Act ("Prop 65").Study highlights individual chemicals as well as consumer product categories that people are likely to be exposed to as individual consumers, in the workplace, and at the population level.Of the 33 Prop 65-listed chemicals that appear in the CARB emissions inventory, 18 were classif ed as "top tier priorities for elimination."Among these, methylene chloride and N-methyl-2pyrrolidone were most prevalent in products across all three population groups.Of 172 consumer product categories, 105 contained Prop 65-listed chemicals.Although these chemicals are known carcinogens and reproductive/developmental toxicants, and remain in widespread use.Manufacturers and regulators should prioritize product categories containing Prop 65-listed chemicals for reformulation or redesign to reduce human exposures and associated health risks.In the home, general cleaners, laundry detergent, dishwashing soap and nail coatings had the largest number of toxic chemicals present.Adhesives, autocare products, cleaners and lubricants rose to the top of concerning workplace exposures.Many known toxic chemicals could be found in use across these categories, meaning people could experience exposures from multiple sources.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.736 | 0.584 |
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".