Quantitative Estimations of the Holocene Erosion due to Seismically Induced Landslides in the SE Altai (Russia) Applying Detailed Profiling and Statistical Approaches
Bibliographic record
Abstract
Earthquakes are some of the most disastrous natural hazards. Coseismic slope failures often significantly contribute to the global damage and may cause most of the casualties related to strong earthquakes. Seismically induced landslides are widespread phenomena within tectonically active mountain terrain. Their abundant occurrence and the large volumes of displaced slope material reveal their great influence on topographic changes. This paper presents new correlations between the earthquake magnitude and the total volume of displaced slope material and between earthquake magnitude and the volume of the largest triggered landslide. These relationships allow us to quantify erosion due to seismically triggered landslides. Calculation of the total volume of displaced slope material is based on the parameters of the largest landslides which could be preserved in the topography for thousands years. This approach was tested in the most seismically active southeastern part of Russian Altai, which is evidenced by numerous giant earthquake-induced paleo-landslides. The total volume of slope material displaced during ancient earthquakes within the Chagan-Uzun river basin, calculated on the basis of statistical correlations, is (3.0-4.3)×10 -1 km 3 , and the Holocene erosion rate due to seismically induced landslides (1.1-3.0)×10 -5 m a -1 . The numerical estimates were verified by calculating the volumes of all detected earthquake-triggered landslides within the Chagan-Uzun river basin and the neighboring Kurai basin on the basis of detailed profiling approach, which are 1.33×10 -1 and 2.25×10 -1 km 3 , respectively. The Holocene erosion rate due to seismically induced landslides in these basins could be estimated at 1.1×10 -5 and 1.4×10 -5 m a -1 , respectively. Thus the Holocene erosion rate of the SE Altai due to earthquake-triggered landslides obtained by applying different techniques can be estimated at (1.1-3.0)×10 -5 m a -1 , which more precisely characterizes topography changes within the Chuya-Kurai system of intermountain depressions and framing ridges.
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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 | 0.001 | 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) | 0.000 | 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".