Integrating Geophysical Investigations in the Design of Engineered Landslide Mitigation Measures
Bibliographic record
Abstract
Summary The A83 trunk road in Argyll and Bute, Scotland, frequently closes due to slope instability, especially at Glen Croe near the Rest and Be Thankful viewpoint. After a major landslide in September 2022, the Scottish Government sought a permanent solution to mitigate the ongoing risk of debris flow hazards to road users. Given the site’s challenging nature, traditional ground investigation methods were limited. Therefore, non-intrusive geophysical surveys were employed, including Primary Wave Seismic Refraction Tomography (P-SRT), Multichannel Analysis of Surface Waves (MASW), Electrical Resistivity Tomography (ERT), and Induced Polarisation-Potential (IP). These methods provided extensive depth to bedrock data in a complex geological environment, enabling the creation of a 3D ground model. This model informs foundation designs, ensuring structures are placed in competent rock. Ultimately, this approach allows for the construction of robust and resilient landslide mitigation structures.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".