Exploring new methods to analyse spatial impact distributions on debris‐flow fans using data from south‐western British Columbia
Bibliographic record
Abstract
Abstract Predicting the spatial impact of debris flows on fans is challenging due to complex runout behaviour. Debris flow mobility is highly variable and flows can sporadically avulse the channel. For hazard and risk assessments, practitioners typically base the probability of spatial impact or avulsion on their experience and expert judgement. To support decision‐making with empirical observations, we studied spatial impact distributions on 30 active debris‐flow fans in south‐western British Columbia, Canada. We mapped 146 debris‐flow impact areas over an average observation period of 74 years using orthorectified airphotos, satellite imagery, topographic base maps, LiDAR data, orthophotos, and field observations. We devised a graphical method to convert our geospatial mapping into spatial impact heat maps normalized by fan boundaries, enabling comparison of runout distributions across different fans. About 90% of the mapped debris flows reached beyond the mid‐points of fans, while less than 10% avulsed more than half‐way across the fan relative to the previous flow path. Most avulsions initiated at distances of 20% to 40% of the maximum fan length from the fan apex and upstream of the fan intersection point. Large volume events tend to be more mobile in the down‐fan direction, but the relation between volume and cross‐fan runout (e.g., avulsions) is more complex. Differences in spatial impact distributions can be explained, in part, by the degree of fan incision and whether a fan is truncated at its toe by a river or lake. There were no significant differences in spatial impact distributions based on the geology of the source area, sediment supply condition, or hydrogeomorphic process classification.
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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".