Highly contaminated river otters (Lontra canadensis) are effective biomonitors of environmental pollutant exposure
Bibliographic record
Abstract
Abstract River otters (Lontra canadensis) are apex predators that bioaccumulate contaminants via their diet, potentially serving as biomonitors of watershed health. They reside throughout the Green-Duwamish River, WA (USA), a watershed encompassing an extreme urbanization gradient, including a US Superfund site slated for a 17-year remediation. The objectives of this study were to document baseline contaminant levels in river otters, assess otters’ utility as top trophic-level biomonitors of contaminant exposure, and evaluate the potential for health impacts on this species. We measured a suite of contaminants of concern, lipid content, nitrogen stable isotopes (δ15N), and microsatellite DNA markers in 69 otter scat samples collected from twelve sites. Landcover characteristics were used to group sampling sites into industrial (Superfund site), suburban, and rural development zones. Concentrations of polychlorinated biphenyls (PCBs), polybrominated diphenyl ether flame-retardants (PBDEs), dichlorodiphenyl-trichloroethane and its metabolites (DDTs), and polycyclic aromatic hydrocarbons (PAHs) increased significantly with increasing urbanization, and were best predicted by models that included development zone, suggesting that river otters are effective biomonitors, as defined in this study. Diet also played an important role, with lipid content, δ15N or both included in all best models. We recommend river otter scat be included in evaluating restoration efforts in this Superfund site, and as a potentially useful monitoring tool wherever otters are found. We also report ΣPCB and ΣPAH exposures among the highest published for wild river otters, with almost 70% of samples in the Superfund site exceeding established levels of concern.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".