Modern to millennium-old greenhouse gases emitted from ponds and lakes of the Eastern Canadian Arctic (Bylot Island, Nunavut)
Bibliographic record
Abstract
Abstract. Ponds and lakes are widespread across the rapidly changing permafrost environments. Aquatic systems play an important role in global biogeochemical cycles, especially in greenhouse gas (GHG) exchanges between terrestrial systems and the atmosphere. The source, speciation and emission rate of carbon released from permafrost landscapes are strongly influenced by local conditions, hindering pan-Arctic generalizations. This study reports on GHG ages and emission rates from aquatic systems located on Bylot Island, in the continuous permafrost zone of the Eastern Canadian Arctic. Dissolved and ebullition gas samples were collected during the summer season from different types of water bodies located in a highly dynamic periglacial valley: polygonal ponds, collapsed ice-wedge trough ponds, and larger lakes. The results showed strikingly different ages and fluxes depending on aquatic system types. Polygonal ponds were net sinks of dissolved CO2, but variable sources of dissolved CH4. They presented the highest ebullition fluxes, 1 or 2 orders of magnitude higher than from other ponds and lakes. Trough ponds appeared as substantial GHG sources, especially when their edges were actively eroding. Both types of ponds produced modern to hundreds of years old (< 550 yr BP) GHG, even if trough ponds could contain much older carbon (> 2000 yr BP) derived from freshly eroded peat. Lakes had small dissolved and ebullition fluxes, however they released much older GHG, including millennium-old CH4 (up to 3500 yr BP) from lake central areas. Acetoclastic methanogenesis dominated at all study sites and there was minimal, if any, methane oxidation in gas emitted through ebullition. These findings provide new insights on GHG emissions by permafrost aquatic systems and their potential positive feedback effect on climate.
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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 | 0.002 | 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".