Applications of Remote Sensing for Characterizing Debris Transfer Processes
Bibliographic record
Abstract
The transfer of debris by geomorphic processes in mountainous terrain can pose a hazard to the safe operation of linear infrastructure. These geohazards have the potential to cause billions of dollars of economic losses and loss of life. The prediction of occurrence, magnitude, and travel distance of these geohazards still remains a major scientific challenge. An understanding of processes of erosion and deposition during these events are required to better understand these geohazards. The primary aim of this thesis is to use terrestrial laser scanning (TLS) to characterize debris transfer processes along transportation corridors. Long-term TLS scanning campaigns in the Thompson River Valley in British Columbia, Canada was undertaken to investigate these processes and the timeframes over which these cycles can occur. In the first part of the thesis, a series of three post-glacial river terraces in the Thompson River Valley were investigated. The evolution of these landforms was documented using high-resolution TLS point clouds. Slope process models were then generated of the failure mechanisms contributing to cliff-face failure using change detection across a seven-year TLS record. Depositional patterns and distributions of granular sediment on the talus slope was also investigated. The second part of the thesis focused on steep catchment monitoring in the White Canyon. A detailed rockfall database of over 72,000 events spanning seven years was generated from TLS change detection. The rockfall database was linked to catchment recharge and transfer processes occurring at this site. The length of the study period was demonstrated to be a key consideration, permitting observations of different periods of heightened geomorphic activity on the study slope. A suite of point cloud tools and techniques were generated to help facilitate these analyses, as reported in the appendices. The work presented in this thesis has contributed to the need for quantitative field observations in the realm of debris movement research. In addition, this thesis has presented the first work on characterizing the present evolution of post-glacial river terraces in the lower-Thompson River Valley.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".