Variations in Stable Isotope Fractionation of Hg in Food Webs of Arctic Lakes
Bibliographic record
Abstract
Biotic and abiotic fractionation of mercury (Hg) isotopes has recently been shown to occur in aquatic environments. We determined isotope ratios (IRs) of Hg in food webs (zooplankton, chironomids, Arctic char) and sediments of 10 Arctic lakes from four regions and investigated the extent of Hg isotope fractionation. Hg IRs were analyzed by multicollector inductively coupled plasma mass spectrometry (MC-ICP/MS). Hg mass independent fractionation (MIF; Delta(199)Hg) and mass dependent fractionation (MDF; delta(202)Hg) were calculated and compared among samples. IRs of Hg in sediment were characterized mainly by MDF and low MIF (Delta(199)Hg -0.37 to 0.74 per thousand). However, all biota showed evidence of MIF, most pronounced in zooplankton (Delta(199)Hg up to 3.40 per thousand) and char (Delta(199)Hg up to 4.87 per thousand). Zooplankton takes up highly fractionated MeHg directly from the water column, while benthic organisms are exposed to sedimentary Hg, which contains less fractionated Hg. As evidenced by delta(13)C measurements, benthic chironomids make up a large proportion of char diet, explaining in part why MIF(char) < MIF(zooplankton) in lakes, where both samples were measured. Hg IRs in char varied among regions, while char from lakes from each region showed similar degrees of MIF. A MIF-offset was derived representing the mean MIF difference between sediment and fish, and indicated that fish in two regions retain sediment signatures altered by a consistent offset. Due to its minimal lake-to-catchment area and very high water retention time ( approximately 330 years), the meteor impact crater lake (Pingualuk) reflects a "pure" atmospheric Hg signature, which is modified only by aqueous in-lake processes. All other lakes are also affected by terrestrial Hg inputs and sediment processes.
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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 | 0.001 |
| 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) | 0.000 | 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".