Rare and dangerous: Recognizing extra-ordinary events in stream channels
Bibliographic record
Abstract
Extreme-value statistics has taught us that flood flows can be estimated reasonably well, and that while extreme flows are rare, they are certain to occur. The physical process of flooding is reasonably well understood. However, this knowledge does not extend to steep creeks with potentially highly mobile beds. Most infrastructures on such creeks have been designed for clearwater floods with return periods of up to 200 years. This does not account for hydrogeomorphic processes such as debris floods and debris flows in which parts of, or the entire, channel bed sediments are mobilized and lead to massive erosion of channel bed and banks and debris inundation on terminal alluvial fans. Similarly, the potential for outburst floods – many times larger than normal floods – related to failure of landslide, glacier, moraine, beaver or man-made dams is not systematically included in standard hazard assessments. This paper has the objective of bridging science and practice by highlighting some of the most threatening hydrogeomorphic hazards to which people and infrastructure in mountain regions are exposed, and provides suggestions on how practice can be improved to properly diagnose and analyze the potential for such unusual floods. It is hoped that this will reduce potential losses in spite of the continued encroachment of urban and industrial development into mountain terrain.
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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 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".