Decadal differences in polycyclic aromatic compound (PAC) concentrations in two seabird species in Arctic Canada
Bibliographic record
Abstract
Seabirds are exposed to a variety of environmental contaminants in the Arctic. While the persistence, bioaccumulation, and toxicity of some groups of contaminants have been well-studied in seabirds since the 1970s, there is less known about polycyclic aromatic compounds (PACs). With increased vessel traffic, and potential oil and gas development in the Arctic region, there is a need to understand existing PAC exposure in biota against which to compare potential effects of anticipated increases of PACs in the marine region. Thick-billed murres (Uria lomvia) and northern fulmars (Fulmarus glacialis) collected in the Baffin Bay - Davis Strait region during the International Polar Year (IPY; 2007-08), and during a recent Strategic Environmental Assessment (2018; SEA) were examined for hepatic PAC concentrations. We found that fulmars generally had higher concentrations of PACs than the murres, but murres and fulmars sampled in 2007/08 had higher concentrations of most groups of PACs compared to birds from 2018. The one exception to this pattern was that the sum of the alkylated congeners of the heterocyclic aromatic compounds containing a sulfur atom (dibenzothiophene; ΣAHET) was significantly higher in murres in the more recent sampling period (2018) as compared to 2007/08. ΣAHETs likely reflect recent exposure to more refined petroleum products associated with small boats, such as diesel, gasoline and motor oil. This work highlights the need for longitudinal studies on PAC concentrations in biota for us to gain a better understanding of how Arctic biota are exposed to this group of contaminants, and the potential deleterious effects associated with PACs.
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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 | 0.001 |
| 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".