Modelling discontinuity control on the development of Hell’s Mouth landslide
Bibliographic record
Abstract
Abstract This paper focuses on numerical modelling and back analysis of the Hell’s Mouth landslide to provide improved understanding of the evolution of a section of the north coast of Cornwall, UK. Discontinuity control is highlighted through the formation of a ‘zawn’ or inlet, the occurrence of two successive landslides and evidence of ongoing instability through opening of tension cracks behind the cliff top. Several integrated remote sensing (RS) techniques have been utilised for data acquisition to characterise the slope geometry, landslide features and tension crack extent and development. In view of the structural control on the rock slope failures, a 3D distinct element method (DEM) code incorporating a discrete fracture network and rigid blocks has been adopted for the stability analysis. The onset and opening of tension cracks behind the modelled slope failure zones has also been studied by analysing the displacements of two adjoining landslide blocks, between which, a joint-related tension crack developed. In addition, a sensitivity analysis has been undertaken to provide further insight into the influence of key discontinuity parameters (i.e. dip, dip direction, persistence and friction angle) on the stability of this section of the coastline. Numerical modelling and field observations indicate that block removal and preferential erosion along a fault resulted in the formation of the inlet. The development of the inlet provides daylighting conditions for discontinuities exposed on the inlet slope wall, triggering the initial landslide which occurred on 23rd September 2011. Numerical modelling, and evidence from a video of the initial landslide, suggests that the cliff instability is characterised by a combination of planar sliding, wedge sliding and toppling modes of failure controlled by the discrete fracture network geometry.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".