Environmental risk assessment of polycarboxylate polymers used in cleaning products in the United States
Bibliographic record
Abstract
Polycarboxylate polymers have been common components of consumer and institutional cleaning products for decades. With interest heightened in the potential environmental impact of polymers, the American Cleaning Institute, the industry trade association of the cleaning products industry in the United States, is reassessing the state of the science regarding the environmental safety of polymers in cleaning products. In this case study, acrylic acid homopolymers and acrylic acid-maleic acid copolymers are evaluated using historical ecotoxicity data that have been reported over the past three decades. The evaluation includes an environmental exposure assessment that is based on recent information regarding the occurrence of those ingredients in cleaning products and market sales data for cleaning products sold in the United States. The ecotoxicity of polycarboxylate polymers is generally low. Consequently, the potential environmental risks associated with their use in cleaning products in the United States are low even when applying very conservative assumptions to the environmental exposure assessment. In addition, there are recent supporting conclusions from assessments by the governments of Australia and Canada that polycarboxylate polymers are polymers of low concern, and the U.S. Environmental Protection Agency has included a number of polycarboxylate polymers among the ingredients on its Safer Chemical Ingredients List based on their low hazard profile.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".