Combining Long-Range Transport and Bioaccumulation Considerations to Identify Potential Arctic Contaminants
Bibliographic record
Abstract
The identification of potential Arctic contaminants requires an assessment of both the long-range transport and the bioaccumulation of the chemicals, most particularly in the indigenous inhabitants of the Arctic. For this purpose, a nonsteady state, zonally averaged global distribution model was linked to a nonsteady state bioaccumulation model describing Inuit exposure from a marine diet. The potential of hypothetical, perfectly persistent chemicals with varying combinations of partitioning properties to enrich in the Arctic environment following emission in the lower latitudes and, additionally, to bioaccumulate in the Arctic food chains was evaluated using the Arctic contamination and bioaccumulation potential (AC-BAP). The AC-BAP is defined as the quotient of the human body burden of the chemical and the quantity of chemical cumulatively emitted to the global environment. The highest AC-BAP values (up to 3.7 x 10(-11) person(-1)) were obtained for hypothetical multimedia chemicals with intermediate volatility and hydrophobicity. Perfectly persistent chemicals with 3.5 < log K(OW) < 8.5 and log K(OA) > 6 had AC-BAP values of at least 10% of the maximum value, indicating that a broad range of chemicals are potential Arctic contaminants if they are persistent. Moreover, the simulation results suggest that a chemical's potential to bioaccumulate has a stronger impact on the overall potential to become an Arctic contaminant in humans than its potential for long-range transport. This modeling exercise demonstrates how linking nonsteady state models of chemical bioaccumulation and of global chemical fate can provide a valuable tool for assessing a chemical's potential to be a contaminant in remote regions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".