Predicting Bioavailability and Accumulation of Organochlorine Pesticides by Japanese Medaka in the Presence of Humic Acid and Natural Organic Matter Using Passive Sampling Membranes
Bibliographic record
Abstract
Adsorption to dissolved organic matter (DOM) may significantly decrease the freely dissolved concentration of many hydrophobic organic compounds and, hence, result in reduced bioavailability to aquatic organisms. Here, the suitability of using triolein-embedded cellulose acetate membrane (TECAM) as a biomimetic surrogate to assess the bioavailability of organochlorine pesticides (OCPs) in water in the presence of DOM was explored. The accumulation of OCPs was measured in TECAM and pelagic Japanese medaka (Oryzias latipes) in the laboratory after 12 h exposure to water containing different levels of Aldrich humic acid. Further, OCP uptake by TECAM and medaka in real aqueous environments was evaluated after 30 d exposures in two sites. Laboratory results showed that OCP uptake by medaka consistently decreased with increasing levels of humic acid in the range of 0-15 mg C/L in sample solutions. This tendency was closely mimicked by OCP accumulation in TECAM under the same conditions. Field results showed that TECAM accumulated similar OCP patterns as medaka (r2 = 0.92 for site 1 and r2 = 0.94 for site 2), although comparison of the in-field eight OCP concentrations in TECAM to those in medaka yielded approximately a factor of 3 (on a wet weight basis). These results suggest that the TECAM method can be used as a simple and useful tool to predict the bioavailability and bioaccumulation potential of poorly biotransformed organic compounds in pelagic fish in aqueous environment.
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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) | 0.000 | 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".