Prioritization of risks posed by synthetic chemicals manufactured in China toward humans and the environment via persistence, bioaccumulation, mobility and toxicity properties
Bibliographic record
Abstract
Over a third of the global chemical production and sales occurred in China, which make effective assessment and management for chemicals produced by China's chemical industry essential not just for China but for the world. Here, we systematical assessed the persistence (P), bioaccumulation (B), mobility (M) and toxicity (T) potency properties for the chemicals listed in Inventory of Existing Chemical Substances of China (IECSC) via experimental data retrieved from large scale databases and in silico data generated with well-established models. Potential PBT, PMT and PB&MT substances were identified. High risk potentials were highlighted for groups of synthetic intermediates, raw materials, as well as a series of biocides. The potential PBT and PMT synthetic intermediates and/or raw materials unique to the IECSC were dominated with organofluorines, for example, the intermediates used as electronic light-emitting materials. Meanwhile, the biocides unique to the IECSC were mainly organochlorines. Some conventional classes of insecticides, such as organochlorines and pyrethroids, were classified as being of high concern. We further identified a group of PB&MT substances that were considered to be both "bioaccumulative" and "mobile". Their properties and common substructures for several major clusters were characterized. The present results prioritized groups of substances with high potentials to cause adverse effects to the environment and humans, many of which have not yet been fully recognized.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".