Seasonal patterns in greenhouse gas emissions from thermokarst lakes in Central Yakutia (Eastern Siberia)
Bibliographic record
Abstract
Abstract In the ice‐rich permafrost area of Central Yakutia (Eastern Siberia, Russia), climate warming and other natural and anthropogenic disturbances have caused permafrost degradation and soil subsidence, resulting in the formation of numerous thermokarst (thaw) lakes. These lakes are hotspots of greenhouse gas emissions, but with substantial spatial and temporal heterogeneity across the Arctic. We measured dissolved CO2 and CH4 concentrations in thermokarst lakes of Central Yakutia and their seasonal patterns over a yearly cycle. Lakes formed over the Holocene (alas lakes) are compared to lakes that developed over the last decades. The results show striking differences in dissolved greenhouse gases (up to two orders of magnitude) between lake types and seasons. Shallow lakes located in hydrologically closed alas depressions acted as CO2 sinks and strong sources of diffusive CH4 during some seasons (ebullition was not assessed). Recent thermokarst lakes were moderate to extremely high sources of diffusive CO2 and CH4, with considerable accumulation of greenhouse gas under the ice cover (winter) or in the deepest water layers (summer), highlighting the need to include spring and autumn as critical periods for integrated assessments. The water column was stratified in winter (all lake types) and especially in summer (recent thermokarst lakes), generating anoxia in bottom waters and favoring CH4 production and storage, particularly in the most organic‐rich lakes. The diffusive fluxes measured from thermokarst lakes of this typical taiga alas landscape of Central Yakutia are among the highest presented across Arctic and subarctic regions.
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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 | 0.000 | 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) | 0.001 | 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".