Seismic Monitoring of a Slope to Investigate Topographic Amplification
Bibliographic record
Abstract
Some earthquakes with a magnitude lower than Ms 7.0, such as Ludian earthquake in Yunnan in 2013, have triggered strong secondary geo-hazards in the form of slope failures. Topographic amplification is generally considered to be the main causal factor for these slope failures. However, until recently, this idea is not supported by appropriate seismic monitoring data. The Kangding Ms 6.3 earthquake on November 22 nd , 2014 was monitored in Lengzhuguan, Sichuan Province, located 56 km from the earthquake epicenter. Six monitoring instruments have recorded this earthquake. The horizontal and vertical component Peak Ground Acceleration (PGA), the site response directivity, the directional variation of the Arias intensity, and the acceleration response spectrum were determined from the data obtained. Conclusions could be drawn that the topographic amplification effect of the isolated ridge on the right bank was stronger than that of nearly linear slope on the left bank and the topographic amplification effect at a slope break was stronger than on a linear slope.
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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 | 0.000 | 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".