Screening New Persistent and Bioaccumulative Organics in China’s Inventory of Industrial Chemicals
Bibliographic record
Abstract
Over a third of the world's annual chemical production and sales occur in China. Thus, knowledge of the properties of the substances produced and emitted there is important from a global perspective. The chemical Inventory of Existing Chemical Substances of China (IECSC) lists over 45 000 chemicals. When compared to the North American and European chemical inventories, 6916 substances were found to be unique to the IECSC. We retrieved structural information for 14 938 organic chemicals in the IECSC and determined their overall environmental persistence , bioaccumulation factor (BAF), and long-range transport potential (transfer efficiency) using in silico approaches with the goal of identifying new chemicals with properties that might lead to global contamination issues. Overall, 10% of the 14 938 chemicals were unique to the IECSC and their environmental persistence and BAF were statistically higher than the values for the rest of the IECSC chemicals. We prioritized 27 neutral organic compounds predicted to have prolonged environmental persistence, and high potential for bioaccumulation and long-range transport when compared with polychlorinated biphenyls as a benchmark. We also identified 69 organofluorine compounds with three or more perfluorinated moieties, unique to the IECSC. Screening approaches and results from this study help to identify and prioritize those to be considered in further environmental modeling and monitoring assessments.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".