Polycyclic aromatic compounds (PACs) and trace elements in four marine bird species from northern Canada in a region of natural marine oil and gas seeps
Bibliographic record
Abstract
There is a growing understanding of how oil pollution can affect aquatic ecosystems, including physical and chemical effects. One of the biggest challenges with detecting the effects of oil-related contaminants on biota from resource development is understanding the background levels and potential effects of the exposure of biota to contaminants from various natural and anthropogenic sources prior to large scale oil and gas operations. Seabirds are effective indicators of pollution, and can be useful for tracking oil-related contaminants in the marine environment. We sampled four seabird species (black guillemot, Cepphus grylle; thick-billed murre, Uria lomvia; black-legged kittiwake, Rissa tridactyla; and northern fulmar, Fulmarus glacialis) in the Baffin Bay-Davis Strait region of the Northwest Atlantic and Arctic oceans, an area where natural oil and gas seeps are present but lacking any large-scale oil and gas projects. We found detectable levels of PACs and several trace elements in all species examined. Alkylated PAC levels were higher than parent compounds in all four seabird species examined, with fulmars and murres having the highest levels detected; mean hepatic concentrations of ∑16PAC were 99.05, 46.42, 12.78 and 9.57 ng/g lw, respectively, for guillemots, murres, fulmars and kittiwakes. Overall, PAC concentrations in the seabird species examined were similar to PAC concentrations measured in other bird species in regions with more industrialization. These findings provide data which can be used to assess the current oil-related contaminant exposure of biota in the region. As well, they provide background levels for the region at a time when shipping activity is relatively low, which can used for future comparisons following expected increases in shipping and oil and gas activities in the region.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".