An Arctic Terrestrial Food-Chain Bioaccumulation Model for Persistent Organic Pollutants
Bibliographic record
Abstract
A model representing the bioaccumulation of persistent organic pollutants (POPs) in arctic terrestrial mammalian food-chains is developed, parametrized, tested, and analyzed. The model predicts concentrations of POPs in lichen, caribou (Rangifer tarandus), and wolf (Canis lupus) food-chains of Canada's central and western arctic region from measured concentrations in air and snowpack meltwater. The model accounts for temporal and seasonal variation in diet composition, life-stage, body weight, and fat content over the life-span of the animal. Model predicted concentrations of 25 organic chemicals forecasted for caribou and wolves from Cambridge Bay (69 degrees 07' N 105 degrees 03' W), Inuvik (68 degrees 18' N 133 degrees 29' W) and Bathurst Inlet (64 degrees 15' N 113 degrees 07' W) are shown to be in good agreement with the observed data. The model illustrates a strong relationship between biomagnification factors and chemical K(OA) and illustrates the effect of age, sex, and temperature on POPs bioaccumulation. Model results show that POPs with K(OA)s < 10(5) do not biomagnify in arctic terrestrial food-chains, while substances that exhibit log K(OA)s > 5 and also exhibit a log K(OW) > 2, show significant bioaccumulation in arctic terrestrial food-chains. The model shows that persistent low K(OW) (K(OW)s < 10(5)) but high K(OA) substances such as beta-HCH, 1,2,4,5 tetrachlorobenzene, and beta-endosulfan biomagnify in terrestrial mammals.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 0.001 |
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".