Geophysical Characteristics of Permafrost Degradation across Boreal Landscapes after Disturbance by Fire or Water
Bibliographic record
Abstract
Summary Fire and hydrology can be significant drivers of permafrost change in boreal landscapes, altering the availability and transport of soil carbon and nutrients that have important implications for future climate and ecological succession. However, not all landscapes are equally susceptible to disturbance by fire or hydrological processes. As fire frequency is expected to increase in the high latitudes, methods to understand the vulnerability and resilience of different landscapes to permafrost degradation are needed. We present a combination of multi-scale remote sensing, geophysical, and field observations that reveal details of both near-surface (<1 m) and deeper impacts of fire and hydrology on permafrost. Along 42 transects that collectively span more than 6,000 m at 31 sites located in different landscape settings within interior Alaska, subsurface geophysical imaging indicates locations where permafrost appears to be resilient to disturbance from fire or small streams, areas where warm permafrost conditions exist that may be most vulnerable to future change, and also where permafrost has thawed after fire or because of nearby surface water. Data collected along each transect include observations of active layer thickness (ALT), organic layer thickness (OLT), plant species cover, electrical resistivity tomography (ERT), and downhole Nuclear Magnetic Resonance (NMR) measurements. In addition, we discuss 300 km of newly acquired airborne electromagnetic (AEM) data in the western part of the lake-rich Yukon Flats that extends the coverage of an earlier 2010 AEM survey into a more ice-rich region. AEM data are used to evaluate the relationship between surface water features and deep (up to 100 m or more) permafrost extent in order to evaluate the potential for subsurface hydrologic connectivity and the potential for lateral fluxes of water.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 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".