Mobilization of geochemical elements in the active layer of permafrost to surface water in Russian Arctic
Bibliographic record
Abstract
The predicted increasing ground temperatures in the Arctic results in the deepening of the active layer and intensification of geochemical processes, which could affect the geochemical composition of surface waters. Determining the responses of the riparian soil systems to surrounding hydrological flows under changing climate conditions is important for understanding the seasonal changes in hydrological processes. Therefore, in this study, one soil core from the study area polygon rim (close to the Taz River, TA) and two soil cores from the riverain terrace (close to the Syoyakha River, SY and Murtyyakha River, MU) in Russian Western Siberia and their supra-permafrost water, adjacent stream flows and river water were sampled for analysis of geochemical elements. The results showed that most elements above their respective detection limits (Mn, Sr, Fe, Mg, Cr, Co, V, Pb, Al and Ca) started to accumulate in the downwards gleyed layer during September–October in response to the deepest thaw in the active layer. This study focused on the highly mobile elements, i.e., Mn, Ca, Mg, Al and Ti, in the deepest layer; and found the transport of organic matter in the upper layer carried these elements to both surface water ponds/flows and supra-permafrost water, and further, to the rivers. The best linear correlation for both stream flows and river water were Mn, which may be a proxy for predicting the processes occurring within the active layer during the annual summer-autumn thaw. Finally, landscapes with different ice contents may experience changes in the elements transported to surface waters.
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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".