Seasonal patterns in greenhouse gas emissions from lakes and ponds in a High Arctic polygonal landscape
Bibliographic record
Abstract
Abstract Lakes and ponds can be hotspots for CO2 and CH4 emissions, but Arctic studies remain scarce. Here we present diffusive and ebullition fluxes collected over several years from 30 ponds and 4 lakes formed on an organic‐rich polygonal tundra landscape. Water body morphology strongly affects the mixing regime—and thus the seasonal patterns in gas emissions—with ice‐out and autumnal turnover periods identified as hot moments in most cases. The studied thermokarst lake maintained relatively high ebullition rates of millennia‐old CH4 (up to 3405 14C YBP). Larger and deeper kettle lakes maintained low fluxes of both gases (century to millennium‐old), slowly turning into a CO2 sink over the summer. During winter, lakes accumulated CO2, which was emitted during the ice‐out period. Coalescent polygonal ponds, influenced by photosynthesizing benthic mats, were continuous CO2 sinks, yet important CH4 emitters (modern carbon). The highest fluxes were recorded from ice‐wedge trough ponds (up to 96 mmol CO2 equivalent m−2 d−1). However, despite clear signs of permafrost carbon inputs via active shore erosion, these sheltered ponds emitted modern to century‐old greenhouse gases. As the ice‐free period lengthens, scenarios of warmer and wetter conditions could favor both the production of CO2 and CH4 from thawing permafrost carbon, and CH4 production from recently fixed carbon through an atmospheric CO2‐to‐CH4 shunt at sites in which primary production is stimulated. This must be carefully considered at the landscape scale, recognizing that older carbon stocks can be mineralized efficiently in specific locations, such as in thermokarst lakes.
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 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 | 0.000 | 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".