Polycyclic aromatic compound and trace metal element residues in Mytilus mussels at marine wildlife hotspots on the Pacific coast of Canada
Bibliographic record
Abstract
The Pacific coast of Canada has a rich marine fauna and a growing human population with increasing potential for pollution releases, but there is currently little overlap between marine wildlife hotspots and ongoing biomonitoring efforts for less bioaccumulative contaminants such as polycyclic aromatic compounds (PAC) and trace metals (metals). We surveyed PACs and metals at marine bird breeding colonies in coastal British Columbia in 2018 by analyzing chemical residues in the soft tissue of bivalve Mytilus sp. mussels collected from stations (n = 3) at seven sites. The concentration of sum PACs (∑43PAC) and high molecular weight (HMW) PACs were highest at the Second Narrows colony in Vancouver Harbour, a highly urbanized and industrialized port within the Salish Sea. For conservation areas, two Salish Sea and three Pacific Ocean coast colonies, PACs were generally lower. However, ∑43PAC, ∑HMWPAC, and several HMW congeners at the remote site of Triangle Island, a Marine National Wildlife Area, were not significantly different from Second Narrows. The dominant PAC sources at all sites are likely pyrogenic rather than petrogenic, as suggested by PAC profiles, proportion of parent PACs, and source-indicator congeners. For metals, site differences were found for seven out of eight priority metals, but principal component analysis indicated that site differences, such as high mercury and cadmium at offshore sites, are likely related to environmental and biological variables including salinity, condition index, water temperature, and shell length. Our survey across a broad coastal region shows that PAC and metal biomonitoring programs with mussels should include wildlife hotspots where the exposure of protected vertebrate species to pollutants with low bioaccumulation potential would be less obvious, and shows that collection of data on key covariates (e.g. lipid content, salinity) will be critical to tracking long-term trends and detecting pollution release events.
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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 | 0.002 | 0.001 |
| 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".