A Terrestrial Food-Chain Bioaccumulation Model for POPs
Bibliographic record
Abstract
Mechanistic bioaccumulation models for fish and piscivorous food-webs are widely used to assess the environmental hazard and risk of commercial chemicals, develop water quality criteria and remediation objectives, and conduct exposure assessment of pesticides in aquatic systems. Similar models for mammals and terrestrial food-webs are largely absent. As a result, the hazards and risks of bioaccumulative substances in mammals, birds, and humans remain unrecognized by regulators, and current globally used criteria for identifying bioaccumulative substances only apply to water-breathing organisms and are inadequate for protecting air-breathing organisms including mammals, birds, and human beings. In this paper, we develop and test a modeling framework that can be used to estimate the biomagnification potential and the organism-soil bioaccumulation factor of organic commercial chemicals in terrestrial food-chains. We test the model for the soil-earthworm-shrew food-chain and apply the model to illustrate that (i) chemicals with an octanol-air partition coefficient (K(OA)) < 10(5.25) do not biomagnify even if the K(ow) is high and optimal for biomagnification in fish; (ii) chemicals with a K(OA) > or = 105.25 and a K(ow) between 10(1.75) and 10(12) have a biomagnification potential unless they are metabolized at a sufficiently rapid rate (e.g., in excess of 0.3 d(-1) or a half-life time of 2.5 d for shrews).
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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 | 0.002 |
| 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 | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".