Brief Overview: A Draft Framework for Risk-Based Prioritization & Evaluation of Additives & Polymer-associated Chemistries
Bibliographic record
Abstract
Widespread global use of polymers and plastic has led to increased stakeholder interest in the health & environmental assessments of these materials. Formal frameworks (i.e., RISK21, Canadian ERC) may require specific data types, developed with prescribed methods, and largely focused on non-polymeric substances. These data are not always available and can result in inaction, or conservative decision making. This is due to the propensity for regulatory or policy decisions to rely primarily on hazard information, without comprehensively considering exposure information. Assessing risk and making risk-based decisions requires deliberate consideration of a complex, and often dynamic, suite of information. For risk assessments that are highly challenging (e.g., large numbers of substances), approaches which screen and prioritize data inputs is a practical methodology for parsing out and addressing complexity in a stepwise approach. This framework for screening and prioritizing additives and polymer-associated chemistries (APAC) was developed to parameterize the large, diverse set of chemistries that are utilized in the manufacture and processing of polymers by considering relative chemical hazards and potential polymer-related exposure profiles together to form the initial basis for a practical and informed risk assessment. The framework leverages existing datasets, mines global databases, and utilizes predictive modeling methodology in a tiered approach to move toward the future goal of fit-for-purpose polymers risk assessments, and is intended to be an accessible approach for all stakeholders with interest in polymers risk assessment.The purpose of communicating this abbreviated draft approach via the Center for Open Science is to allow for rapid, high-level dissemination of key framework elements to allow for public feedback via the platform. Feedback on potential for broad utility by stakeholders across a range of experience levels, as well as potential for framework uptake related to risk-based initiatives, are of particular interest. Input from stakeholders in the form of the survey response(s) (https://app.sli.do/event/eXhC9yG6WQrQnFuFzb2NsR) may be considered for incorporation into the finalized, peer-review submission of the full framework.
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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 | 0.020 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.025 | 0.014 |
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".