Bioaccumulation and Transport of Contaminants: Migrating Sockeye Salmon As Vectors of Mercury
Bibliographic record
Abstract
Biological transport by migratory animals is increasingly recognized as important to the long-range dispersal of toxic contaminants. Mercury (Hg) contamination is a widespread environmental concern with serious health implications for humans and wildlife. Due to their unique life history, anadromous salmon may act as important vectors for this contaminant, transferring Hg between marine and freshwater ecosystems. Previous analyses have considered contaminant transport by salmon to be unidirectional. These studies have evaluated Hg import to freshwater by spawning adults, but have not quantitatively assessed export through the migration of juveniles to the ocean. To determine the total Hg burden to freshwater systems by sockeye salmon, we reconstructed the net transport of Hg to the Wood River System in Bristol Bay, Alaska accounting for fluxes in (via adults) and out (via juveniles) of the system. Hg concentrations were higher in juvenile than adult salmon. Hg export from freshwater systems by salmon ranged from 3 to 30% of total import. Proportional export by smolts may be higher for populations under heavy exploitation with strong density dependence in juvenile recruitment. Full consideration of contaminant loading by migratory species requires attention to the relative contaminant flux at all life history stages and the effects of density dependent growth and survival.
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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 | 0.000 | 0.000 |
| 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".