Toward understanding the contribution of waterbodies to the methane emissions of a permafrost landscape on a regional scale-A case study from the Mackenzie Delta, Canada
Bibliographic record
Abstract
Abstract Waterbodies in the arctic permafrost zone are considered a major source of the greenhouse gas methane (CH4) in addition to CH4 emissions from arctic wetlands. However, the spatio‐temporal variability of CH4 fluxes from waterbodies complicates spatial extrapolation of CH4 measurements from single waterbodies. Therefore, their contribution to the CH4 budget of the arctic permafrost zone is not yet well understood. Using the example of two study areas of 1,000 km² each in the Mackenzie Delta, Canada, we approach this issue (i) by analyzing correlations on the landscape scale between numerous waterbodies and CH4 fluxes and (ii) by analyzing the influence of the spatial resolution of CH4 flux data on the detected relationships. A CH4 flux map with a resolution of 100 m was derived from two aircraft eddy‐covariance campaigns in the summers of 2012 and 2013. We combined the CH4 flux map with high spatial resolution (2.5 m) waterbody maps from the Permafrost Region Pond and Lake Database and classified the waterbody depth based on Sentinel‐1 SAR backscatter data. Subsequently, we reduced the resolution of the CH4 flux map to analyze if different spatial resolutions of CH4 flux data affected the detectability of relationships between waterbody coverage, number, depth, or size and the CH4 flux. We did not find consistent correlations between waterbody characteristics and the CH4 flux in the two study areas across the different resolutions. Our results indicate that waterbodies in permafrost landscapes, even if they seem to be emission hot spots on an individual basis or contain zones of above average emissions, do currently not necessarily translate into significant CH4 emission hot spots on a regional scale, but their role might change in a warmer 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".