Yukon-Kuskokwim River Delta 2015 fire burn depth measurements and unburned soil and vegetation organic matter and carbon content collected in 2019.
Bibliographic record
Abstract
Tundra environments in Alaska are experiencing elevated levels of wildfire, and the frequency is expected to keep increasing due to rapid warming of the Arctic. Because of large amounts of carbon stored in permafrost soils, tundra wildfires may release significant amounts of carbon to the atmosphere that ultimately influence the Earth’s radiative balance. Therefore, accounting for the amount of carbon released from tundra wildfires is important for understanding the trajectory of climate change. We collected data in the Yukon-Kuskokwim River Delta during the summer of 2019 for the purpose of determining organic matter and carbon lost during the 2015 fire season. Organic matter and carbon lost from combustion were determined by combining burn depth measurements with organic matter and carbon content measurements from unburned tundra. Burn depth measurements were taken opportunistically across different levels of burn severity. Three vegetative markers, Sphagnum fuscum, Eriophorum, and Dicranum spp., that survived the fire event were used to measure the difference between the pre and post fire soil height in unburned and burned areas respectively, defined here as burn depth. All burn depth measurements are accompanied with coordinate locations so that they can ground truth and be upscaled by remote sensing data of burn severity. Organic matter and carbon content of the dense live vegetation layer and fibric soil layer were measured in the lab from vegetation and soil cores taken from four different sites in unburned tundra areas.
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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) | 0.002 | 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".