Permafrost degradation and soil erosion as drivers of greenhouse gas emissions from tundra ponds
Bibliographic record
Abstract
Abstract Climate change poses a serious threat to permafrost integrity, with expected warmer winters and increased precipitation, both raising permafrost temperatures and active layer thickness. Under ice-rich conditions, this can lead to increased thermokarst activity and a consequential transfer of soil organic matter to tundra ponds. Although these ponds are known as hotspots for CO 2 and CH 4 emissions, the dominant carbon sources for the production of greenhouse gases (GHGs) are still poorly studied, leading to uncertainty about their positive feedback to climate warming. This study investigates the potential for lateral thermo-erosion to cause increased GHG emissions from small and shallow tundra ponds found in Arctic ice-wedge polygonal landscapes. Detailed mapping of fine-scale erosive features revealed their strong impact on pond limnological characteristics. In addition to increasing organic matter inputs, providing carbon to heterotrophic microorganisms responsible for GHG production, thermokarst soil erosion also increases shore instability and water turbidity, limiting the establishment of aquatic vegetation—conditions that greatly increase GHG emissions from these aquatic systems. Ponds with more than 40% of the shoreline affected by lateral erosion experienced significantly higher rates of GHG emissions (∼1200 mmol CO 2 m −2 yr −1 and ∼250 mmol CH 4 m −2 yr −1 ) compared to ponds with no active shore erosion (∼30 mmol m −2 yr −1 for both GHG). Although most GHGs emitted as CO 2 and CH 4 had a modern radiocarbon signature, source apportionment models implied an increased importance of terrestrial carbon being emitted from ponds with erosive shorelines. If primary producers are unable to overcome the limitations associated with permafrost disturbances, this contribution of older carbon stocks may become more significant with rising permafrost temperatures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.008 | 0.001 |
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; both teacher heads agree on what is shown here.
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".