Mercury partitioning in super-permafrost groundwater, Truelove Lowland, Devon Island, Nunavut
Bibliographic record
Abstract
The objective of this study was to determine the dominant biogeochemical controls on mercury partitioning in super-permafrost groundwater at Truelove Lowland, Devon Island, Nunavut. Mercury partitioning in snow, ephemeral standing water, and super-permafrost groundwater was investigated. Results indicate that partitioning differs between matrices, and that particulate mercury is spatially and temporally dynamic in Truelove Lowland groundwater. Particulate mercury in groundwater was 73 % of total mercury, while snow had only 22 % particulate mercury. Particulate mercury in groundwater rose by over 20 % from Julian day 181 to 189, and decreased slightly on Julian day 191. No single geochemical parameter was a good predictor of particulate mercury concentrations. To expand upon the findings of the field study a laboratory microcosm study was conducted to determine whether certain biogeochemical processes influence mercury partitioning in super-permafrost groundwater. Particulate mercury in the dissimilatory iron reducing bacteria inhibited microcosm was 61 % of total mercury, approximately 18 % lower than in all other treatments. Iron (III) concentrations had a positive correlation with particulate mercury while chloride concentrations had a negative correlation with particulate mercury. Sulfate reducing bacteria were not found to influence mercury partitioning.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".