No deceleration signs in the permafrost ground subsidence four years after the 2019 fire in Northwest Territories, Canada
Bibliographic record
Abstract
Abstract The circum-arctic permafrost environment is often disturbed by wildfires but could also show resilience to these disturbances. However, the increased frequency and extent of wildfires, coupled with unprecedented hot weather, have introduced greater uncertainties in the post-fire permafrost dynamics. We need to address emerging questions, e.g. How will permafrost respond to the joint effect of hot anomalies and wildfires? To what extent will post-wildfire deformation evolve? How will permafrost resilience to wildfires vary? Utilizing interferometric synthetic aperture radar time series analysis, we investigated the post-wildfire ground deformation around a 2019 fire scar in the lower Mackenzie Valley, Northwest Territories, Canada, where dramatic heat anomalies and severe wildfires have been recorded in recent years. The resilience of permafrost to wildfires appears to be weakened by the continuous and rapid warming after the fire, as evidenced by the year-on-year acceleration in subsidence rates. Such acceleration was never reported by previous findings that typically observed deceleration in subsidence rates four to five years after wildfires. The deformation along the line of sight (LOS) of the satellite demonstrates significant permafrost degradation induced by wildfires and exacerbated by climate warming, and the cumulative subsidence was detected up to 25 cm in the LOS direction in the upland areas and up to 10 cm in the lowland areas four years after the fire. The difference in deformation magnitude could be attributed to local factors, including ground ice, topography, and vegetation. Our study highlights the increasingly severe threat to circum-arctic permafrost due to the combined effects of wildfires and extreme heat anomalies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".