Fate-directed risk assessment of chemical mixtures: a case study for cedarwood essential oil
Bibliographic record
Abstract
The environmental risk assessment of UVCBs (i.e., substances of unknown or variable composition, complex reaction products, or biological materials) is challenging due to their inherent complexity. A particular problem is that UVCBs can contain constituents with unidentified chemical structures and/or have variable composition of constituents from batch to batch. Moreover, the composition of a UVCB in the environment is not the same as that of the UVCB in a product, meaning that a risk assessment based on environmental exposure to the UVCB in a product does not represent the actual environmental risk. Here we propose an in silico fate-directed risk assessment framework for UVCBs using cedarwood oil as a case study. The framework uses Monte Carlo simulations and the mass-balance models SimpleTreat and RAIDAR to provide quantitative information on whether unidentified constituents within the physical–chemical property space of a UVCB can be the decisive factor for the environmental risk of the entire UVCB. Thereby the framework provides a robust decision tool to evaluate if a UVCB risk assessment requires additional tests or if the data on known constituents is representative for the risk of the entire UVCB. In the case of cedarwood oil, it could be shown that a risk assessment based on the known constituents (representing around 70% of the overall UVCB by weight) is representative for the environmental risk of the entire UVCB – reducing the need for additional testing and test animals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 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".