Bioaccumulation of newly deposited mercury by fish and invertebrates: an enclosure study using stable mercury isotopes
Bibliographic record
Abstract
Enriched stable mercury (Hg) isotopes were added to four 10 m diameter enclosures in Lake 239 at the Experimental Lakes Area to increase inorganic Hg loading. Our main objectives were to (i) follow low-level additions (spikes) of isotope-enriched Hg through the biogeochemical cycle and into the food web and (ii) determine the relative contribution of newly deposited Hg to methyl Hg (MeHg) accumulation by fish and other biota. The experiment ran for two summers (2000, 2001), with different enriched Hg isotopes being added each year. Within 1 month of beginning additions in 2000, spike Hg was detected in water, zooplankton, and benthic invertebrates as MeHg, and in fish as total Hg (THg; the sum of inorganic and organic Hg). In 2001, concentrations in water of inorganic spike Hg added in 2000 were near detection limits, but concentrations of 2000 spike MeHg in water and biota remained unchanged or greater. Despite comparatively large increases in inorganic Hg loading, accumulation of ambient, non-spike MeHg predominated in all organisms, and spike MeHg never comprised more than 15%, even after 1 year. Our results suggest that changes in Hg loading will affect MeHg concentrations in fish and other biota, but that steady state may not be achieved for at least 10–30 years under conditions similar to our enclosures.
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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".