Innovative use of GIS and drone photogrammetry for cliff stability modelling
Bibliographic record
Abstract
Evaluating the condition and stability of coastal cliffs and assessing longevity of significant overhangs and undercuts in an active marine environment is a challenge. As part of cliff management studies undertaken for the Coastal Zone Management Unit of the government of Barbados, a programme of innovative cliff profile mapping was conducted around the island of Barbados. A combination of land-based field mapping information and fixed wing aircraft collected high-resolution LiDAR imagery was utilised for cliff geometry assessments, supplemented for specific cliff profiles by fill-in high-precision three-dimensional (3D) digital terrestrial imagery, captured by low level wide wingspan UAV drone flights. Comprehensive photogrammetric processing of this imagery, combined with detailed geographic information system software evaluation of the collected point cloud data, allowed generation of full 3D wireframe digital terrain and digital elevation models (DTMs and DEMs). Multiple representative cliff areas around the island were identified, allowing thorough 3D stability assessment to be accomplished of key problematic areas using Flac3D. Vertical cross-section profiles were also cut so that undercut and notched cliffs could be analysed in detail in two dimensions using Voronoi tessellation approaches applied within the universal distinct element code UDEC as a means to replicate the characteristics of the vuggy coralline limestone cliffs.
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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 | 0.001 |
| 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".