Estimating velocity from noisy GPS data for investigating the temporal variability of slope movements
Bibliographic record
Abstract
Abstract. Knowledge of processes and factors affecting slope instability is essential for detecting and monitoring potentially hazardous slopes. Knowing the timing of acceleration or deceleration of slope movements can help to identify important controls and hence to increase our process understanding. For this methods to derive reliable velocity estimations are important. The aim of this study was to develop and test a method to derive velocities based on noisy GPS data of various movement patterns and variable signal-to-noise-ratio (SNR). Derived velocities represent reliable average velocities representative for a given period. The applied smoothing windows directly depends on the SNR of the data, which is modeled using Monte Carlo simulation. Hence, all obtained velocities have a SNR above a predefined threshold and for each velocity period the SNR is known, which helps to interpret the temporal variability. In sensitivity tests with synthetic time-series the method was compared to established methods to derive velocities based on GPS positions, including spline and Kernel regression smoothing. Those sensitivity tests clearly demonstrated that methods are required that adopt the time window to the underlying error of the position data. The presented method performs well, even for a high noise levels and variable SNR. Different methods were further applied to investigate the inter-annual variability of permafrost slope movements based on daily GPS- and inclinometer data. In the framework of the new method, we further analyzed the error caused by a rotation of the GPS mast (hmast = 1.5 m). If the tilting is higher than its uncertainty, the rotational movement can be separated and the direction of movement became more uniform. At one GPS station, more than 12% of the measured displacement at the antenna was caused by the rotation of the station.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".