Mercury accumulation in sediments of Lhù’ààn Mânʼ (Kluane Lake, YT): Response to past hydrological change
Bibliographic record
Abstract
Northern lakes provide many ecosystem services, including the provision of traditional foods and clean water. These systems are vulnerable to climate-driven changes in hydrology and contaminant accumulation, but the direction and magnitude of projected changes are poorly constrained. One contaminant of concern is mercury; currently, we cannot accurately predict how mercury accumulation in lakes will respond to climate-induced changes, especially in lakes with glacial inflows and complex hydrology. Sediment cores collected from two regions of a glacially fed lake (Lhù’ààn Mân’; Kluane Lake, Yukon, Canada) were analyzed to investigate controls on sediment mercury accumulation in the context of previously described hydrological changes. Differences in catchment contributions drove differences in sediment mercury accumulation between lake regions during the Duke River hydrological period (ca. 750–1650). During the more recent Slims River hydrological period (ca. 1650–2015), mercury accumulation did not differ between regions, and mercury was delivered to the lake primarily via catchment organic matter and carbonate-rich sediments from the largest, glacially derived inflow (Slims River). Recent climate-induced geomorphic change caused loss of the main lake inflow (Slims River) in 2016, making Kluane Lake an ideal system for future investigations of how loss of glacial inflow will affect mercury accumulation in northern lakes.
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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 | 0.001 |
| 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".