Climatic Influence on Temporal Trends of Polychlorinated Biphenyls and Organochlorine Pesticides in Landlocked Char from Lakes in the Canadian High Arctic
Bibliographic record
Abstract
Temporal trends and climate related parameters affecting the fate of legacy persistent organic pollutants (POPs) such as polychlorinated biphenyls (PCBs) and organochlorine pesticides (OCPs) were examined in landlocked Arctic char from four lakes in the Canadian Arctic. Among biological parameters, lipid content was a key factor explaining the concentration of most POPs in Arctic char. Legacy PCBs and OCPs generally showed declining trends of concentrations in Arctic char, consistent with past restriction on uses and emissions of POPs. However, increases in lake primary productivity (measured as chlorophyll a) exerted a dilution effect on POPs concentrations in Arctic char. Concentrations of POPs in char from the last two decades were positively correlated with interannual variations of the North Atlantic Oscillation (NAO). Higher concentrations of POPs in Arctic char were observed in 3 of the 4 lakes during positive NAO phases. This, together with increasing local Arctic temperatures, could lead to increases on POPs concentrations in char from remote Arctic Lakes in future decades. Also, if there are nearby secondary sources as may be the case for Resolute Lake, located near an airport where increasing levels were found for hexachlorobenzene and toxaphene, probably due to the mobilization from secondary sources in soils.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".