The Potential Use of Synthetic Aperture Radar for Estimating Methane Ebullition From Arctic Lakes<sup>1</sup>
Bibliographic record
Abstract
Abstract: Arctic lakes are significant emitters of methane (CH4), a potent greenhouse gas, to the atmosphere; yet no rigorous quantification of the magnitude and variability of pan‐Arctic lake emissions exists. In this study, we demonstrate the potential for a new method using synthetic aperture radar (SAR) imagery to detect methane bubbles in lake ice to scale up whole‐lake measurements of CH4 ebullition (bubbling) to regional scales. We estimated ebullition from lakes, which is often the dominant mode of lake emissions, by mapping the distribution of bubble clusters frozen in early winter ice across surfaces of seven tundra lakes and one boreal forest lake in Alaska. Applying previously measured ebullition rates associated with four distinct classes of bubble clusters found in lake ice, we estimated whole‐lake emissions from individual lakes. The percent surface area of lake ice covered with bubbles (R2 = 0.68) and CH4 ebullition rates from lakes (R2 = 0.59) and were correlated with radar return values from RADARSAT‐1 Standard Beam mode 3 for the tundra lakes, suggesting that with appropriate scaling and consideration for variability in lake‐ice conditions, this technique has the potential to be used for estimating broader‐scale regional and pan‐Arctic lake methane emissions.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".