The time for ambitious action is now: Science-based recommendations for plastic chemicals to inform an effective global plastic treaty
Bibliographic record
Abstract
The ubiquitous and global ecological footprint arising from the rapidly increasing rates of plastic production, use, and release into the environment is an important modern environmental issue. Of increasing concern are the risks associated with at least 16,000 chemicals present in plastics, some of which are known to be toxic, and which may leach out both during use and once exposed to environmental conditions, leading to environmental and human exposure. In response, the United Nations member states agreed to establish an international legally binding instrument on plastic pollution, the global plastics treaty. The resolution acknowledges that the treaty should prevent plastic pollution and its related impacts, that effective prevention requires consideration of the transboundary nature of plastic production, use and pollution, and that the full life cycle of plastics must be addressed. As a group of scientific experts and members of the Scientists' Coalition for an Effective Plastics Treaty, we concur that there are six essential "pillars" necessary to truly reduce plastic pollution and allow for chemical detoxification across the full life cycle of plastics. These include a plastic chemical reduction and simplification, safe and sustainable design of plastic chemicals, incentives for change, holistic approaches for alternatives, just transition and equitable interventions, and centering human rights. There is a critical need for scientifically informed and globally harmonized information, transparency, and traceability criteria to protect the environment and public health. The right to a clean, healthy, and sustainable environment must be upheld, and thus it is crucial that scientists, industry, and policy makers work in concert to create a future free from hazardous plastic contamination.
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 | 0.066 | 0.090 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.011 | 0.025 |
| Scholarly communication | 0.033 | 0.050 |
| Open science | 0.011 | 0.019 |
| Research integrity | 0.096 | 0.090 |
| Insufficient payload (model declined to judge) | 0.029 | 0.018 |
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".