Temporal changes in soil water retention in burned and unburned areas after a wildfire and implications for slope stability
Bibliographic record
Abstract
Temporal changes in the hydrologic and mechanical behavior of burned slopes result in changes in post-wildfire slope stability. The lack of long-term monitoring data on landscape recovery hinders our understanding of the interdependencies between wildfire-induced alterations in soil and vegetation properties, near-surface processes, and landslides. This study presents results from a 4-year field monitoring project of soil water retention and the corresponding long-term changes in landslide susceptibility after the 2019 Williams Flats Wildfire on the Colville Indian Reservation, WA, USA. Water retention was measured near both a burned and an unburned tree at multiple depths. The measured data were integrated into a suction stress-based infinite slope stability analysis to determine the corresponding temporal changes in shallow landslide susceptibility. The results showed that the lowest factor of safety (FS) near the burned tree was in June of the first year, which was attributed to macropores acting as preferential flow paths. The FS near the burned tree increased in the following years, which was attributed to vegetation regrowth. Vegetation recovery was quantified using normalized difference vegetation index mean values and soil temperature data. Field observations supported contributions of the macropore clogging on temporal changes in slope stability. The results suggest a dynamic interaction of vegetation recovery and soil physical changes and discuss the challenges of quantifying short- and long-term post-wildfire slope stability.
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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 | 0.001 |
| 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".