Kinematics of post-wildfire debris flow initiation mechanism: impact of hydrophobic layer spatial variability and particle size
Bibliographic record
Abstract
Post-wildfire debris flow events have been more frequent due to climate change and increased wildfire adversity. Changes in noncohesive soil hydrophobicity, burn, and vegetation removal during wildfires dramatically enhance slopes’ vulnerability to rainfall-induced failure in the arid Southwest USA and similar areas worldwide. This paper contributes to a better understanding the hydrophobic sand slope failure mechanism using experiments and theory. In total, 36 indoor raining experiments use different configurations of fine, medium, and coarse hydrophobic sands subjected to various rain intensities on different slope inclinations. Specific findings contribute to understanding how the spatial variability of the post-wildfire hydrophobic layer, concerning depth and the sand grain size variations, affects the slope failure mechanisms. Clarifying slope failure mechanisms in different layouts is crucial for understanding the initiation and further spatiotemporal debris flow evolution. Results indicate that the surficial hydrophobic layer fails because erosion develops simultaneously from many small failure patches. Erosion pattern, time to failure, water, and sediment overflow temporal dynamics correlate to the dominant sand grain size. Furthermore, seepage-induced shallow infinite slope occurs when the hydrophobic sand layer develops below the surface. Finally, this study identifies the fine sand as the most vulnerable to failure in both configurations.
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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 | 0.000 | 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 | 0.000 |
| 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 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".