Tundra recovery post-fire in the Yukon–Kuskokwim Delta, Alaska
Bibliographic record
Abstract
Abstract The extent of wildfires in tundra ecosystems has dramatically increased since the turn of the 21st century due to climate change and the resulting amplified Arctic warming. We simultaneously studied the recovery of vegetation, subsurface soil moisture, and active layer thickness (ALT) post-fire in the permafrost-underlain uplands of the Yukon–Kuskokwim Delta in southwestern Alaska to understand the interaction between these factors and their potential implications. We used a space-for-time substitution methodology with 2017 Landsat 8 imagery and synthetic aperture radar products, along with 2016 field data, to analyze tundra recovery trajectories in areas burned from 1953 to 2017. We found that spectral indices describing vegetation greenness and surface albedo in burned areas approached the unburned baseline within a decade post-fire, but ecological succession takes decades. ALT was higher in burned areas compared to unburned areas initially after the fire but negatively correlated with soil moisture. Soil moisture was significantly higher in burned areas than in unburned areas. Water table depth (WTD) was 10 cm shallower in burned areas, consistent with 10 cm of the surface organic layer burned off during fire. Soil moisture and WTD did not recover in the 46 years covered by this study and appear linked to the long recovery time of the organic layer.
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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 | 0.001 | 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".