The impacts of regional Arctic lake change on remotely sensed methane emission hotspots in Alaska, USA
Bibliographic record
Abstract
Arctic and boreal wetlands and lakes are experiencing complex ecological changes as a result of warming. The potential for rapidly thawing permafrost to promote large increases in methane (CH4) emissions via ground subsidence and ponding (thermokarst), and permafrost carbon mineralization is of particular concern for accelerating the permafrost carbon feedback (PCF) [Turetsky et al. 2020]. However, complex hydrological dynamics produce large uncertainties regarding the sign and magnitude of carbon loss in modeling and forecasting efforts. Determining CH4’s current and future contributions to the PCF is challenging due to sparse observations, high spatiotemporal variability, and heterogeneous Arctic landscapes. As a result, top-down (observation-based) and bottom-up (model/inventory-based) evaluations of annual Arctic and boreal CH4 emissions disagree by 50-200% [McGuire et al. 2012; Peltola et al. 2019]. Constraining the current budget and forecast uncertainty in future Arctic emissions will require scale-bridging approaches that reconcile fine spatiotemporal variability and regional to continental scale coverage. To this end, we compiled multiple large remote sensing datasets to study relationships between Arctic lake and thermokarst landscape morphology trends with remotely-sensed (AVIRIS-NG) CH4 emission hotspot detections. Preliminary analyses from a lake-and-wetland-rich 1,750 km2 study area of the Yukon Kuskokwim Delta, AK, USA reveal discrete correlations between recently wetted areas and CH4 hotspot detections. However, in an analysis of over 1,200 lakes > 1 ha, hotspots were detected in greater abundance surrounding lakes that have shrunk in area since 1999 (p < 0.05) (Fig. 2). Our preliminary results imply a complex response of CH4 emissions surrounding dynamic permafrost environments. Ongoing analyses seek to further elucidate patterns related to waterbody size, permafrost ice and carbon contents, and relationships between terrestrial and limnetic hotspot detections.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".