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 CO 2 and CH 4 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 CH 4 (up to 3405 14 C YBP). Larger and deeper kettle lakes maintained low fluxes of both gases (century to millennium‐old), slowly turning into a CO 2 sink over the summer. During winter, lakes accumulated CO 2 , which was emitted during the ice‐out period. Coalescent polygonal ponds, influenced by photosynthesizing benthic mats, were continuous CO 2 sinks, yet important CH 4 emitters (modern carbon). The highest fluxes were recorded from ice‐wedge trough ponds (up to 96 mmol CO 2 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 CO 2 and CH 4 from thawing permafrost carbon, and CH 4 production from recently fixed carbon through an atmospheric CO 2 ‐to‐CH 4 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.
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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) | 0.003 | 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 teacher head, 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".