Bioconcentration and Aquatic Toxicity of Superhydrophobic Chemicals: A Modeling Case Study of Cyclic Volatile Methyl Siloxanes
Bibliographic record
Abstract
Many chemicals in commerce are classified as "superhydrophobic", having log octanol-water partition coefficients (log KOW) approaching or exceeding 7. Examples include long-chain alkanes, halogenated aromatics, and cyclic volatile methylsiloxanes (cVMS). We show that superhydrophobic chemicals present unique assessment challenges because of their sparing solubility in water and difficulties in empirical determinations of bioconcentration factors (BCFs) and aquatic toxicity. Using cVMS as an example, BCFs are considerably lower than expected due to biotransformation. Reviewed aquatic toxicity test data for cVMS in a range of aquatic organisms show little or no toxic effects up to solubility limits in water and sediment. Explanations for this apparent lack of toxicity of cVMS, and by extension to other superhydrophobic chemicals, are explored using a conventional one-compartment uptake model to simulate bioconcentration and toxicity tests using an assumed baseline narcotic critical body residue (CBR) and a range of organism sizes. Because of the low aqueous concentrations, equilibration times are very long and BCFs are sensitive to even very slow rates of biotransformation. Most organisms fail to achieve the assumed CBR during feasible test durations even at the solubility limit. Regulatory evaluation of superhydrophobic substances requires specially designed test protocols addressing biotransformation and dietary uptake.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".