Are Past Wildfire Activities Correlated with the Frequency of Different Types of Post-Wildfire Geomorphic Events such as Landslides and Debris Flows
Bibliographic record
Abstract
Around the world, the frequency and intensity of wildfire events are rapidly increasing. Such a trend increasingly exposes some communities to the risk of the secondary hazards of wildfire, such as post-wildfire geomorphic events like a landslide and debris flow. To better manage the risk imposed by the secondary hazards of wildfire, a better understanding of the relationship between wildfire activities and the geomorphic events related therewith is necessary. In this paper, the temporal relationship between past wildfire events and the frequency of two different types of geomorphic events (landslides and debris flow) was studied statistically through the conduction of a time series analysis. The analysis conducted using the data created by the construction of a geomorphic event inventory derived from a set of Landsat based land cover classification of the region of the Rocky and Omineca Mountains in the Canadian province of British Columbia ranging from 1986 to 2019, and the wildfire boundary data provided by the Federal Government of Canada. Both a simple linear regression and a Poisson regression were conducted to analyze the relationship between time and the frequency of geomorphic events, with the application of a slope-based stratification. The study has three key findings. The first finding is that overall, there exists a strong negative temporal correlation between the time elapsed since the most recent event of a wildfire and the frequency of post-wildfire geomorphic events. Second, slope appears to be an influential factor for the relationship between wildfire and the frequency of post-wildfire geomorphic events. Third, despite the presence of a strong correlation, time is a very poor variable for explaining the dynamics of the frequency of post-wildfire geomorphic events.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".