Regional debris‐flow and debris‐flood frequency–magnitude relationships
Bibliographic record
Abstract
Abstract Construction of frequency–magnitude (F–M) relationships of debris floods and debris flows is challenging because of few direct observations, discontinuous event occurrence, loss of field evidence, the difficulty of accessing the sediment archive and the challenge of finding suitable statistical methods to analyse the dataset. Consultants often face budget limitations that prohibit application of the full gamut of absolute dating methods, stratigraphic analysis and analytical tools necessary to fully resolve the F–M legacy. In some cases, F–M curves are needed for watersheds without local information, or where obtaining this information is prohibitively expensive. For such watersheds, the F–M relationship may be estimated where several F–M curves have already been assembled in a specific region. Individual F–M curves are normalized by fan area or fan volume, then stratified by process type and geomorphic activity level. This paper describes the development of regional F–M curves for debris flows in southwestern British Columbia and debris flows and debris floods in the Bow River valley near Canmore, Alberta. We apply the regional relationships to other cases in Canada and the United States and demonstrate that the method can be globalized. The regional approach is compared to cases where detailed F–M relationships have been established by other means. Strong negative deviations from the regional debris‐flow or debris‐flood magnitude trends could signal inherent watershed stability, while strong positive deviations could signal extraordinary landslide processes, or suggest that the fan may be largely of paraglacial origin. We highlight some of these outlying cases and develop a method whereby the regional curves can be meaningfully adjusted, or reliance can be placed on lower or upper confidence bounds of the F–M curves. We caution against the indiscriminate use of the regionally based F–M curves, especially in watersheds where multiple geomorphic processes are active. © 2020 John Wiley & Sons, Ltd.
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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 | 0.000 |
| 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 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".