Improving the Environmental Risk Assessment of Substances of Unknown or Variable Composition, Complex Reaction Products, or Biological Materials
Bibliographic record
Abstract
Substances of unknown or variable composition, complex reaction products, or biological materials (UVCBs) pose unique risk assessment challenges to regulators and to product registrants. These substances can contain many constituents, sometimes partially unknown and/or variable, depending on fluctuations in their source material and/or manufacturing process. International regulatory agencies have highlighted the difficulties in characterizing UVCBs and assessing their toxicity and environmental fate. Several industrial sectors have attempted to address these issues by developing frameworks and characterization methods. Based on the output of a 2016 workshop, this critical review examines current practices for UVCB risk assessment and reveals a need for a multipronged and transparent approach integrating whole-substance and constituent-based information. In silico tools or empirical measurements can provide information on discrete and/or blocks of UVCB constituents with similar hazard properties. Read-across and/or whole-substance toxicity and fate testing using adapted emerging methods can provide whole-substance information. Continued collaboration of stakeholders representing government, industry, and academia will facilitate the development of practical testing strategies and guidelines for addressing regulatory requirements for UVCBs. Environ Toxicol Chem 2020;39:2097-2108. © 2020 Health and Environmental Sciences Institute. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".