User manual, source code, and test set for MSBASv3 (Multidimensional Small Baseline Subset version 3) for one- and two-dimensional deformation analysis
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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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.163 | 0.125 |
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".