Mapping Belowground Carbon Pools and Potential Vulnerability in the Yukon-Kuskokwim Delta, Alaska
Bibliographic record
Abstract
Permafrost regions store an estimated half of the global belowground organic carbon pool and twice the global atmospheric carbon level. A warming climate results in increased carbon gas emission, therefore knowing more about the amount and composition of organic carbon stored in permafrost regions is crucial for understanding feedbacks on global climate change. Using the Yukon-Kukskowim (YK) Delta, Alaska as a study site, we quantified belowground carbon pools and their potential vulnerability to release into the atmosphere as greenhouse gasses. We identified relevant landcover classes (burned and unburned upland peat plateaus, wetlands, ponds/lakes) in the YK Delta, from which we quantified total belowground carbon pools (30cm) and assessed the composition of the organic matter using Fourier-transform infrared spectroscopy. To characterize the size and distribution of soil carbon pools in the YK Delta, we built a Random Forest Machine Learning model that mapped the spatial distribution of soil carbon to a depth of 30 cm over a 1910 km2 watershed. The map product was produced in Google Earth Engine and used covariates that include, but are not limited to, Worldview2 high-resolution optical imagery (2m), ArcticDEM (5m), and Sentinel-2 level 1C multispectral imagery (10 m), including NDVI. We found substantial variation across landcover classes in soil characteristics that affect organic matter vulnerability, including gravimetric water content, thaw depth, bulk density, and percent carbon. Compared to upland areas, thaw depths were significantly deeper in wetlands and lakes, where we detected no surface permafrost (to 1m). Soil carbon content (%) was greatest in moss-dominated wetlands; however, these areas also had the lowest bulk density. Carbon pools and organic matter characteristics also varied between burned and unburned areas. Therefore, we expect that carbon vulnerability varies by landcover class and that future carbon emissions are driven by total carbon pools, thaw depths, and composition of the carbon stored in organic matter pools. These carbon pool and vulnerability maps will contribute to better understanding the impacts of subarctic warming and are critical for developing a more accurate assessment of carbon cycling feedbacks from permafrost regions on global climate change.
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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 | 0.000 | 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".