Multi-sensor approach towards understanding debris-flow activity in the Lattenbach catchment, Austria
Bibliographic record
Abstract
Debris flows (DFs) pose a severe risk to Alpine communities and infrastructure. The Lattenbach catchment (basin area 5,3 km², relief 2134 m) in Tyrol, Austria, is an example for an active DF-site with several DFs occurring per year. To improve our understanding of the DF-process cascade in this catchment, we raise the questions: where does the sediment originate, are hillslope processes the drivers for DF-activity, and how is the relationship of rainfall amount and DF-magnitude? We employ an approach that makes use of the data richness of this study site: High resolution ALS and TLS terrain models and aerial photographs are considered to locate significant elevation differences. Furthermore, we performed an in-detail UAV-based surveying campaign of the active channel reaches for the 2019 and 2020 DF-season. Additionally, we use datasets captured by a DF monitoring station (discharge, volume, timing, precipitation) at the catchment outlet to assess triggering rainfall as well as DF-frequency and magnitudes. We find that in the last fifteen years up to three events occurred annually. A single location, where all DFs originate from, is not detectable, indicating a variety of sediment sources is relevant for DF-initiation, including bank failures and channel incision, partly driven by deep-seated landslides that continuously feed the channel with sediment. Between the years 2005 and 2018 the DF-volumes recorded at the catchment outlet varied between about 5.000 m³ (small) and 46.000 m³ (large). A first analysis suggests that there is a prevailing “background noise“ pattern of relatively small DF-events that happen regularly during every DF-season. We hypothesize that rare, very large events represent a tipping point in the catchment system, which leads to a period of increased large-scale DF-activity over following seasons.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".