Real-time GPS landslide monitoring under poor satellite visibility
Bibliographic record
Abstract
Among the available technologies such as cables and lasers, the Global Positioning System (GPS) is being increasingly used for automated continuous monitoring of landslides and avalanches. The timely identification of precursory movements of landslides could save lives and minimise collateral damage. Carrier-phase observations from four or more GPS satellites allow relative displacements to be measured with centimetre accuracy. However, signals from four satellites with good geometry are not always guaranteed, as the landslide sites are often located along mountain slopes, which are subject to poor satellite visibility. Such landslide locations may, therefore, experience several minutes to hours of positioning discontinuity for some periods of the day. The effect of the availability of satellite signals is greater for sites located on northfacing slopes due to GPS orbit characteristics. We have investigated a method for detecting a displacement of the order of millimetres under poor satellite visibility. We estimate the displacement without differencing the positioning results, supposing that a landslide occurs along the slope in the direction of maximum inclination (this assumption could be later replaced with a landslide outbreak model for a particular site). First, we investigated the method to detect a landslide with only 2 satellites (1 misclosure vector) and then, the improvement of the positioning results when more satellites are available. In this paper, we discuss our algorithms permitting continuous landslide monitoring for low visibility observations and some results of field tests. We discuss specific aspects of our investigations using field data simulating landslides: 1) multipath elimination, 2) estimation of displacement supposing a priori knowledge of the antenna location at the monitoring site, and 3) the necessary time span of observations for detecting landslides.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".