Relationships between organic matter composition and methyl mercury content of offshore and carbon-rich littoral sediments in an oligotrophic lake
Bibliographic record
Abstract
Relationships between organic matter (OM) compounds and methyl mercury concentrations ([MeHg]) have been examined in littoral and offshore sediments of Lake Lusignan (Québec). The highest [MeHg] were generally found at the sedimentwater interface with exceptionally high concentrations at littoral sites (5.8 ± 1.3 ng·g dry weight1), which were four times more elevated than at offshore sites (1.6 ± 0.77 ng·g dry weight1). Source-specific fatty acid (FA) biomarkers identified that littoral sediments contained more than twice as much terrestrial and bacterial OM compounds than offshore sediments, whereas the amount of labile algal OM was three times higher at littoral sites. Results indicate that [MeHg] were higher in the presence of labile OM substrates, and the amount of terrestrial OM compounds could not predict [MeHg]. Correlations between [MeHg] and FA of Desulfovibrio desulfuricans (a sulfate-reducing bacterium producing MeHg) could significantly account for 36% of [MeHg] at offshore sites; however, no significant relationships were found at littoral sites. This study illustrates that the microbial dynamics involved in producing and degrading MeHg in lacustrine sediments are complex and cannot be predicted solely by the quantification of FA biomarkers in D. desulfuricans or by biomarkers in the OM itself.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".