Tissue Distribution of Several Series of Cationic Surfactants in Rainbow Trout (<i>Oncorhynchus mykiss</i>) Following Exposure via Water
Bibliographic record
Abstract
Bioaccumulation assessment is important for cationic surfactants in light of their use in a wide variety of consumer products and industrial processes. Because they sorb strongly to natural surfaces and to cell membranes, their bioaccumulation behavior is expected to differ from other classes of chemicals. Divided over two mixtures, we exposed rainbow trout to water containing 10 alkyl amines and 2 quaternary alkylammonium surfactants for 7 days, analyzed different fish tissues for surfactant residues, and calculated the tissues' contribution to fish body burden. Mucus, skin, gills, liver, and muscle each contributed at least 10% of body burden for the majority of the test chemicals. This indicates that both sorption to external surfaces and systemic uptake contribute to bioaccumulation. In contrast to the analogue alkylamine bases, the permanently charged quaternary ammonium compounds accumulated mostly in the gills and were nearly absent in internal tissues, indicating that systemic uptake of the charged form of cationic surfactants is very slow. Muscle-blood distribution coefficients were close to 1 for all alkyl amines, whereas liver-blood distribution coefficients ranged from 13 to 90, suggesting that the dominant considerations for sorption in liver are different from those in blood and muscle. The significant fraction of body burden on external surfaces can have consequences for bioaccumulation assessment.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".