Spatial and Temporal Assessment of Mercury and Organic Matter in Thermokarst Affected Lakes of the Mackenzie Delta Uplands, NT, Canada
Bibliographic record
Abstract
We examined dated sediment cores from 14 thermokarst affected lakes in the Mackenzie Delta uplands, NT, Arctic Canada, using a case-control analysis to determine how retrogressive thaw slump development from degrading permafrost affected the delivery of mercury (Hg) and organic carbon (OC) to lakes. We show that sediments from the lakes with retrogressive thaw slump development on their shorelines (slump-affected lakes) had higher sedimentation rates and lower total Hg (THg), methyl mercury (MeHg), and lower organic carbon concentrations compared to lakes where thaw slumps were absent (reference lakes). There was no difference in focus-corrected Hg flux to sediments between reference lakes and slump-affected lakes, indicating that the lower sediment Hg concentration in slump-affected lakes was due to dilution by rapid inorganic sedimentation in the slump-affected lakes. Sedimentation rates were inversely correlated with THg concentrations in sediments among the 14 lakes considered, and explained 68% of the variance in THg concentration in surface sediment, further supporting the dilution hypothesis. We observed higher S2 (algal-derived carbon) and particulate organic carbon (POC) concentrations in sediment profiles from reference lakes than in slump lakes, likely because of dilution by inorganic siliciclastic matter in cores from slump-affected lakes. We conclude that retrogressive thaw slump development increases inorganic sedimentation in lakes, and decreases concentrations of organic carbon and associated Hg and MeHg in sediments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".