Development of environmental risk assessment framework and methodology for consumer product chemicals in China
Bibliographic record
Abstract
Environmental risk assessment (ERA) methodologies for consumer product chemicals are well established in most developed regions including the United States, Canada, and the European Union. However, such methodologies are not yet fully developed for emerging economies, such as China. The objective of the present study was to develop an ERA framework involving an exposure methodology using conditions specific to China (i.e., physical setting, infrastructure, and consumers' habits and practice). Incorporated in this newly developed ERA framework for assessing consumer product chemicals were China's current regulatory screening and prioritization schemes as part of a tiered risk assessment approach. The framework started with tier 0, which utilized the existing Chinese regulatory qualitative method; tiers 1 and 2 were quantitative, and used deterministic and probabilistic methods that accounted for per capita residential water usage, wastewater treatment capability, and wastewater/in-stream dilution factors. Due to major differences in wastewater treatment infrastructure and water usage between urban versus rural regions in China, 2 scenarios were identified for quantitatively assessing environmental exposure: 1) urban with wastewater treatment, and 2) rural without wastewater treatment (i.e., direct discharge of wastewater). Our study presents the methodology of the framework with its technical rationale and the companion model Chera, and also provides an overview of the current status of ERA research in China. Environ Toxicol Chem 2019;38:250-261. © 2018 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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".