Determination of Ash Dam Facility Surface Displacement Using InSAR
Bibliographic record
Abstract
This article articulates the determination of Ash Dam Facility Surface Displacement Using InSAR at in South Africa.ADF can fail due to a variety of factors, including structural instability, seepage, or seismic activity.Therefore, using InSAR technology there is a need for threats to be identified and responded to promptly to mitigate consequences such as loss of life and property that can occur from dam failures.Interferometric Synthetic Aperture Radar (InSAR) is a powerful remote sensing technology that has proven invaluable for monitoring the stability and integrity of ash dams.The study used Vertex, which is the Alaska Satellite Facility's (ASF) data search application for remotely sensed imagery of the earth, providing convenient and powerful discovery and download of SAR data, as well as direct access to thematic datasets.ASF Data Search is an easy-to-use search tool for finding SAR data and freely processing higher level SAR products such as InSAR and Auto-RIFT products with ASF's service.The study conducted a time series analysis using Mintpy on the OpenSAR Lab server.The Mintpy toolbox is a Python 3 software for small baseline InSAR time series analysis.The input is a stack of differential interferograms that form a fully connected network.The findings were that the ash dam facility has undergone a total displacement of 200 cm vertically and 310cm laterally.The coordinates in terms of latitude and longitude of this affected area in (7145360,739280) and (7160480, 714480) respectively.In conclusion satellite remote sensing offers a cost and time effective way to monitor large infrastructure assets which would otherwise be a very resource-demanding task via conventional methods.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".