Evidence for Mass-Independent Fractionation of Mercury Isotopes by Microbial Activities Linked to Geographically and Temporally Varying Climatic Conditions in Arctic and Subarctic Lakes
Bibliographic record
Abstract
Cores from Arctic and subarctic Canadian lakes were subjected to isotopic, chemical, micropaleontological, and geochronological analyses for the purpose of investigating mass-independent fractionation (MIF) of mercury isotopes. The cores preserved records of early twentieth century climatic warming (∼1915–1940), subsequent cooling (∼1940–1970), and renewed warming (∼1970–2004) [phases W1, C1, and W2, respectively]. Per mil deviations of 199Hg/202Hg and 201Hg/202Hg ratios due to MIF (Δ199Hg and Δ201Hg values) correlated with biological and biogeochemical factors linked to geographical and temporal climatic variations but varied, in large part, independently of each other. Δ201Hg tended to increase from east to west. Among subarctic lakes this trend paralleled westward decreases in annual precipitation, diatom concentration, and the post-1990 organic carbon/pre-1900 organic carbon ratio, and Δ201Hg increased in the order C1 ≤ W1 < W2. Δ201Hg varied inversely with diatom concentration, but Δ199Hg increased with increasing abundance of cyanobacteria. Arctic lakes, however, showed a south-to-north decrease in Δ199Hg/Δ201Hg ratios, paralleling a decrease in annual precipitation and an increase in Chlorophyta and cyanobacteria. Δ-values of individual lakes depended on the abundances of specific phylogenetic groups of phytoplankton, pyrolysis products of organic matter, and manganese, and on the manganese/iron ratios of oxyhydroxides, displaying clear separation of data representing different climatic trends. These results suggest that MIF was caused by microorganisms, such as bacteria which decomposed dead phytoplankton and mediated oxidation-reduction reactions of manganese and iron, and that the nature and isotope-fractionating activities of the microflora varied with climate-related environmental and biotic factors, including the community structure of the phytoplankton.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".