Management of phthalates in Canada and beyond: can we do better to protect human health?
Bibliographic record
Abstract
Ortho-phthalates (herein referred to as phthalates) are synthetic chemicals used in thousands of different everyday products and materials. Nearly ubiquitous environmental exposure is reflected by phthalate metabolites in the urine of almost all Canadians. However, phthalate exposure tends to be higher amongst people of low socioeconomic status and ethnic minorities. Substantial evidence shows that certain phthalates cause harm to human health, particularly developing fetuses and children. Governments vary in their approach to assessing and managing risks associated with phthalates. Canada continues to take a more permissive stance on phthalate regulations compared to the EU and some US states. We argue that the recent Canadian national risk assessment on phthalates does not appropriately reflect the growing evidence demonstrating harm to human health from phthalate exposure and does not adequately consider the evidence showing higher exposures faced by vulnerable populations. Canadians would benefit from adopting a more stringent regulatory approach to phthalates. Specifically, Canada should expand phthalate restrictions to apply to all consumer products, implement sunset dates toward eliminating the use of existing phthalates, and mandate publicly available evidence of no harm for phthalate alternatives. Canadian alignment on phthalate regulations with the EU and a growing number of US states could encourage other countries to follow suit.
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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".