Bibliographic record
Abstract
Abstract Debris floods have been defined descriptively as mineral and organic sediment‐rich floods, occurring in a steep channel and potentially destabilizing the streambed and banks. While this definition allows one to visualize the process, it does not inform on the mechanics, nor does it recognize different types. We propose to define debris floods as “floods during which the entire bed, possibly barring the very largest clasts, becomes mobile for at least a few minutes and over a length scale of at least 10 times the channel width.” We define the onset of a debris flood by the exceedance of a critical shear stress threshold required to mobilize at least the D 84 of bed material. A threefold classification is proposed in which the first type is triggered by the shear stress exceedance. The second is initiated by transition from a debris flow either in the channel or by oblique impact of a debris‐flow‐prone tributary. In this context we highlight the importance of effective fluid density. The third type is associated with outbreak floods from artificial or natural dams. A further subdivision of debris floods is made by using the ratio of the actual shear stress to the critical shear stress, with higher values indicating damaging and finally catastrophic debris floods in which even preexisting channel bank and bed protection is mobilized. This contribution aims to provide a more succinct mechanistic definition of debris floods that can be implemented in hazard and risk assessments for steep streams and rivers.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".