Seasonal Contrasts in Dissolved Selenium Dynamics in Subarctic Thaw Lakes
Bibliographic record
Abstract
Thermokarst ponds (thaw lakes) are ubiquitous in northern landscapes. They are hotspots for the biogeochemical processing of elements, such as carbon (C), nitrogen (N), sulfur (S), iron (Fe) and manganese (Mn). In turn, those elementary cycles may control the mobility of selenium (Se), an essential micronutrient. To unravel these coupled biogeochemical cycles and identify processes controlling Se mobility, we studied four thermokarst ponds in a subarctic peatland valley influenced by permafrost thaw. The data set comprises of water column and sediment porewater concentration profiles collected during both summer and winter. Physicochemical parameters and dissolved concentrations of major elements, nutrients, and Se were measured and used to model fluxes at the sediment–water interface and to calculate Se speciation. The results suggest that the proximity of the pond from the permafrost structures influenced their biogeochemical dynamics. In the ponds close to permafrost, Se concentrations are 2-fold higher in winter compared to summer, accompanied by an increase in sediment fluxes from 13 to 149 pmol cm –2 yr –1 between summer and winter. The combination of comparatively older dissolved organic matter and of oxygenated conditions explain the seasonal variation in Se concentrations. In the ponds further from the permafrost, Se concentrations are higher, remain unchanged in the water column across seasons, and are linearly correlated with both DOC ( R 2 = 0.64, p < 0.01, n = 50) and Fe ( R 2 = 0.60) concentrations. Thermodynamic calculations show that Se(IV) dominates Se speciation in the porewater at all sites, while the water column reaches saturation with respect to elemental zerovalent Se, suggesting that precipitation of elemental Se could mediate dissolved Se concentrations. Collectively, our results point to the strong control that redox conditions exert on Se mobility, via DOC and Fe, and to the linkages between landscape features, pond physicochemistry, and Se dynamics.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".