Direct Comparison of Insar, UAV & RTK Deformation Methods at North Slide, Thompson River Valley, British Columbia, Canada
Bibliographic record
Abstract
This study focusses on the North Slide, an active landslide that poses a hazard to the national railway traversing the Thompson River valley. The slide acts as an ideal field laboratory for testing and evaluating novel landslide monitoring and evaluation techniques.In this study, we present results of a direct comparison of several commonly used land deformation measurement techniques that have been used to monitor the North Slide for several years. Ground-based Real Time Kinematic (RTK) Global Positioning System (GPS), Unmanned Aerial Vehicle (UAV), and satellite-based Interferometric Synthetic Aperture Radar (InSAR) measurements were collected over the course of two years (2020-2022). Deformation measurements calculated from these repeat surveys are directly compared both spatially and temporally to evaluate their relative accuracy and precision.We find that the InSAR and UAV based deformation measurements agree in terms of movement zone extents and general magnitude. The InSAR measurements more closely reflect the GPS measurements and have a smaller distribution around stationary points than the UAV based deformation. The errors between the UAV and InSAR measurements also appear to scale with the magnitude of deformation. The 1σ and 2σ differences between the UAV and InSAR deformation measurements were 0.025 m/year and 0.051 m/year, respectively.
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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 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".