Susceptibility of Human Populations to Environmental Exposure to Organic Contaminants
Bibliographic record
Abstract
Environmental exposure to organic contaminants is a complex function of environmental conditions, food chain characteristics, and chemical properties. In this study the susceptibility of various human populations to environmental exposure to neutral organic contaminants was compared. An environmental fate model and a linked bioaccumulation model were parametrized to describe ecosystems in different climatic regions (temperate, arctic, tropical, and steppe). The human body burden resulting from constant emissions of hypothetical chemicals was estimated for each region. An exposure susceptibility index was defined as the body burden in the region of interest normalized to the burden of the same chemical in a reference human from the temperate region eating an average diet. For most persistent chemicals emitted to air, the Arctic had the highest susceptibility index (max 520). Susceptibility to exposure was largely determined by the food web properties. The properties of the physical environment only had a marked effect when air or water, not food, was the dominant source of human exposure. Shifting the mode of emission markedly changed the relative susceptibility of the ecosystems in some cases. The exposure arising from chemical use clearly varies between ecosystems, which makes an understanding of ecosystem susceptibility to exposure important for chemicals management.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".