Fecal genotyping and contaminant analyses reveal variation in individual river otter exposure to localized persistent contaminants
Bibliographic record
Abstract
The present study investigated polyhalogenated aromatic hydrocarbon (PHAH) concentrations in feces of known river otters (Lontra canadensis) along the coast of southern Vancouver Island, British Columbia, Canada. Specifically, we combined microsatellite genotyping of DNA from feces for individual identification with fecal contaminant analyses to evaluate exposure of 23 wild otters to organochlorine pesticides (OCPs), polychlorinated biphenyls (PCBs), and polybrominated diphenylethers (PBDEs). Overall, feces collected from otters in urban/industrial Victoria Harbor had the greatest concentrations of nearly all compounds assessed. Fecal concentrations of OCPs and PBDEs were generally low throughout the region, whereas PCBs dominated in all locations. Re-sampling of known otters over space and time revealed that PCB exposure varied with movement and landscape use. Otters with the highest fecal PCB concentrations were those inhabiting the inner reaches of Victoria Harbor and adjacent Esquimalt Harbor, and those venturing into the harbor systems. Over 50% of samples collected from eight known otters in Victoria Harbor had total-PCB concentrations above the maximum allowable concentration as established for Eurasian otter (Lutra lutra) feces, with a geometric mean value (10.6 mg/kg lipid wt) that exceeded the reproductive toxicity threshold (9 mg/kg lipid wt). Those results are consistent with our findings from 1998 and 2004, and indicate that the harbors of southern Vancouver Island, particularly Victoria Harbor, are a chronic source of PCB exposure for otters. The present study further demonstrates the suitability of using otter feces as a noninvasive/destructive biomonitoring tool in contaminant studies, particularly when sampling of the same individuals at the local population-level is desired.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".