Use of Stable Isotope Signatures to Determine Mercury Sources in the Great Lakes
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Sources of mercury (Hg) in Great Lakes sediments were assessed with stable Hg isotope ratios using multicollector inductively coupled plasma mass spectrometry. An isotopic mixing model based on mass-dependent (MDF) and mass-independent fractionation (MIF) (δ 202 Hg and Δ 199 Hg) identified three primary Hg sources for sediments: atmospheric, industrial, and watershed-derived. Results indicate atmospheric sources dominate in Lakes Huron, Superior, and Michigan sediments while watershed-derived and industrial sources dominate in Lakes Erie and Ontario sediments. Anomalous Δ 200 Hg signatures, also apparent in sediments, provided independent validation of the model. Comparison of Δ 200 Hg signatures in predatory fish from three lakes reveals that bioaccumulated Hg is more isotopically similar to atmospherically derived Hg than a lake’s sediment. Previous research suggests Δ 200 Hg is conserved during biogeochemical processing and odd mass-independent fractionation (MIF) is conserved during metabolic processing, so it is suspected even is similarly conserved. Given these assumptions, our data suggest that in some cases, atmospherically derived Hg may be a more important source of MeHg to higher trophic levels than legacy sediments in the Great Lakes.
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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 | 0.000 |
| 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) | 0.000 | 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".