Tracking the behavior of rocky coastal cliffs in northwestern Spain
Bibliographic record
Abstract
Abstract Rocky coastal regions are often inaccessible due to steep slopes and high relief. Remotely sensed data can, therefore, be useful, but they often have low spatial and temporal resolution and, in the case of airborne LiDAR, if not publically available, are costly to obtain. This paper reports on the use of high-resolution images from unmanned aerial vehicles (UAVs) and Structure-from-Motion (SfM) photogrammetric techniques, supplemented by a series of orthophotos and aerial LiDAR, to examine changes in rocky coastal cliffs from 2002 to 2018. The study was conducted over an 800 m-long, orthogneiss-dominated coastal section in northwestern Galicia, Spain. Cliff changes are due, primarily, to rockfalls, resulting from weathering and wave undercutting, which cause talus deposits to accumulate at the cliff foot. These deposits provide temporary protection to the cliff from wave action, until destabilized and removed by wave erosion and shallow landslides. Cliff recession rates are affected by changing conditions within a cycle and are dependent, in part, on when a survey is conducted. The data suggest that rates of cliff recession are increasing in this region and that the plan shape of the coast, which consists of headlands and bays, is continuing to evolve. Most coastal landslides in this region help to transport and dispose of talus fallen from the cliff. In contrast to landslides that remove intact materials from the cliff face, and are, therefore, primary erosional mechanisms, talus landslides are triggered by wave erosion and probably simultaneously, by storm wave spray and splash, rather than by heavy rainfall.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 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 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".