Spatial Patterns of Methylmercury Risks to Common Loons and Piscivorous Fish in Canada
Bibliographic record
Abstract
Deposition of inorganic mercury (Hg) from the atmosphere remains the principle source of Hg contamination for most aquatic ecosystems. Inorganic Hg is readily converted to toxic methylmercury (MeHg) that bioaccumulates in aquatic food webs and may pose a risk to piscivorous fish and wildlife. We conducted a screening-level risk assessment to evaluate the extent of risk to top aquatic piscivores: the common loon (Gavia immer), walleye (Sander vitreus), and northern pike (Esox lucius). Risk quotients (RQs) were calculated on the basis of a dietary Hg exposure indicator (HgPREY) modeled from over 230,000 observations of fish Hg concentrations at over 1900 locations across Canada and dietary Hg exposure screening benchmarks derived specifically for this assessment. HgPREY exceeded benchmark thresholds related to impaired productivity and behavior in adult loons at 10% and 36% of sites, respectively, and exceeded benchmark thresholds for impaired reproduction and health in fishes at 82% and 73% of sites, respectively. The ecozones of southeastern Canada characterized by extensive forest cover, elevated Hg deposition, and poorly buffered soils had the greatest proportion of RQs > 1.0. Results of this assessment suggest that common loons and piscivorous fishes would likely benefit from reductions in Hg deposition, especially in southeastern Canada.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".