Late season mobilization of trace metals in two small Alaskan arctic watersheds as a proxy for landscape scale permafrost active layer dynamics
Bibliographic record
Abstract
Increasing air temperatures in the Arctic have the potential to degrade permafrost and promote the downward migration of the seasonally thawed active layer into previously frozen material. This may expose frozen soils to mineral weathering that could affect the geochemical composition of surface waters. Determining watershed system responses to drivers such as a changing climate relies heavily on understanding seasonal controls on freshwater processes. The majority of studies on elemental concentrations in Arctic river systems have focused on sampling only from spring snowmelt to the summer season. Consequently, there remains a limited understanding of surface water geochemistry, particularly with respect to trace metals, during late fall and early winter. To examine the variability of metal concentrations as a function of seasonality, we measured trace metal concentrations from spring melt to fall freeze-up in 2010 in two high Arctic watersheds: Imnavait Creek, North Slope, Alaska and Roche Mountanee Creek, Brooks Range, Alaska. We focused on aluminum (Al), barium (Ba), iron (Fe), manganese (Mn), nickel (Ni) and zinc (Zn). Concentrations of ‘dissolved’ (< 0.45 μm) Al, Ba, Fe, and Mn in Imnavait Creek waters and Ba in Roche Mountanee waters were highest in late fall/early winter. To link observed surface water concentrations at Imnavait Creek to parent soil material we analyzed the elemental composition of a soil core from the watershed and tracked the soil temperatures as a function of time and depth. The results from this study show a distinct seasonal signature of trace metal concentrations in late fall that correlates with the depth of the thawed active layer.
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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) | 0.000 | 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".