User manual, source code, and test set for MSBASv3 (Multidimensional Small Baseline Subset version 3) for one- and two-dimensional deformation analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Time series of ground deformation are used to describe motion produced by various natural and anthropocentric processes, such as earthquakes, volcanic eruptions, landslides, subsidence due to resource exploitation and uplift due to fluid injection. The Multidimensional Small Baseline Subset version 3 (MSBASv3) software simultaneously processes multiple ascending and descending Differential Interferometric Synthetic Aperture Radar (DInSAR) data sets and produces either one-dimensional, line-of-sight, or two-dimensional, horizontal east-west and vertical, deformation time series with combined temporal resolution. The set of linear equations solved by MSBASv3 is usually rank deficient and is solved in the least-square sense by applying the Singular Value Decomposition (SVD) and the zero, first, or second order Tikhonov regularization. The MSBASv3 source code is written in C++ and is parallelized using OpenMP. It is linked to the Linear Algebra PACKage (LAPACK) library that provides SVD support and to the Geospatial Data Abstraction Library (GDAL) that provides GeoTiff support. To demonstrate the capabilities of the MSBASv3 a test set of ascending and descending RADASAT-2 data over the Barnes Ice Cap (Baffin Island, Nunavut, Canada) during December 2014 - May 2015 is included and is used throughout this user manual to illustrate the processing sequence.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 it