Evaluation of filtering methods for use on high frequency measurements of landslide displacements
Bibliographic record
Abstract
Abstract. Displacement monitoring is a critical control for risks associated with potentially sudden slope failures. Instrument measurements are, however, obscured by the presence of scatter. Data filtering methods aim to reduce the scatter and therefore enhance the performance of early warning systems (EWSs). The effectiveness of EWSs depends on the lag time between the onset of acceleration and its detection by the monitoring system, such that a timely warning is issued for implementation of consequence mitigation strategies. This paper evaluates the performance of three filtering methods (simple moving average, Gaussian-weighted moving average, and Savitzky-Golay), and considers their comparative advantages and disadvantages. The evaluation utilized six levels of randomly generated scatter on synthetic data as well as high-frequency global navigation satellite system (GNSS) displacement measurements at the Ten-mile landslide in British Columbia, Canada. The simple moving average method exhibited significant disadvantages compared to the Gaussian-weighted moving average and Savitzky-Golay approaches. A framework is presented that can be followed to evaluate the adequacy of different algorithms for minimizing monitoring data scatter.
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 | 0.004 | 0.014 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".