Intestinal absorption and biomagnification of organic contaminants in fish, wildlife, and humans
Bibliographic record
Abstract
Methods for the regulatory assessment of the bioaccumulation potential of organic chemicals are founded on empirical measurements and mechanistic models of dietary absorption and biomagnification. This study includes a review of the current state of knowledge regarding mechanisms and models of intestinal absorption and biomagnification of organic chemicals in organisms of aquatic and terrestrial food chains and also includes a discussion of the implications of these models for assessing the bioaccumulation potential of organic chemicals. Four mechanistic models, including biomass conversion, digestion or gastrointestinal magnification, micelle-mediated diffusion, and fat-flush diffusion, are evaluated. The models contain many similarities and represent an evolution in understanding of chemical bioaccumulation processes. An important difference between the biomagnification models is whether intestinal absorption of an ingested contaminant occurs solely via passive molecular diffusion through serial resistances or via facilitated diffusion that incorporates an additional advective transport mechanism in parallel (i.e., molecular ferrying within gastrointestinal micelles). This difference has an effect on the selection of physicochemical properties that best anticipate the bioaccumulative potential of commercial chemicals in aquatic and terrestrial food chains. Current regulatory initiatives utilizing Kow threshold criteria to assess chemical bioaccumulation potential are shown to be unable to identify certain bioaccumulative substances in air-breathing animals. We urge further research on dietary absorption and biomagnification of organic chemicals to develop better models for assessing the bioaccumulative nature of organic chemicals.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".