Field and monitoring data of debris-flow events in the Swiss Alps
Bibliographic record
Abstract
Debris flow is a common process in the Swiss Alps and in other mountainous parts of the world. The understanding of debris-flow behaviour is essential to assess the hazards they present. An important approach towards improving the knowledge of debris-flow processes is the gathering of real-time data by debris-flow observation stations. Observation stations were established in three Swiss debris flow prone watersheds and equipped with video cameras, ultrasonic devices, a radar device, geophones, and rain gauges. In 2000, four significant debris flows were observed. The data provided useful information on the mechanics of debris flows and on the efficiency of the measuring devices. The observed debris flows are characterized by volumes between 5 000 and 35 000 m3, front velocities ranging from 2 to 5 m/s, and peak discharges between 20 and 125 m3/s. The analysis of the monitoring data revealed that ultrasonic and radar devices are very helpful tools, whereas the quality of the geophone signal strongly depends on the substrate on which the instrument is installed (i.e., bedrock versus unconsolidated material). Video images are useful to verify the data obtained by the other devices. A dynamic analysis of one debris flow was carried out and the simulated results are in fair agreement with the observed data.Key words: debris flow, Swiss Alps, monitoring, dynamic analysis.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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 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".