Ground deformation in the Taupo Volcanic Zone, New Zealand, observed by ALOS PALSAR interferometry
Bibliographic record
Abstract
We present ground deformation measurements in the Taupo Volcanic Zone (TVZ) using differential interferomeric synthetic aperture radar (DInSAR) observations collected by ALOS PALSAR during 2006–2010, and compare them with displacement observations from continuous GPS. We acquired and processed DInSAR images from two ascending paths (324 and 325) and one descending path (628) covering the TVZ, and produced linear deformation rates and time series of deformation. The DInSAR results were improved by using a modified version of the small baseline subset (SBAS) algorithm that simultaneously solves for deformation rates and residual topographic noise. The accuracy of the DInSAR displacement rates along line-of-sight to the satellite is 0.5–2 cmyr−1 depending on the number of SAR images and their coherence. We found good agreement between the DInSAR-derived displacement rates and those measured by continuous GPS for the two ascending paths (correlation 0.94 ± 0.01 and 0.89 ± 0.02); the DInSAR uncertainties were too large to make a useful comparison for the descending path (correlation 0.66 ± 0.03). We identified ground deformation due to groundwater and steam extraction for geothermal power. To demonstrate the geophysical application, we modelled the deformation results using simplified sources for some of the geothermal signals using ellipsoidal and tabular approximations.
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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 | 0.001 |
| 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 | 0.001 |
| 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 | 0.000 |
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".