Experiences with a mixed-mode GPS-based volcano monitoring system at Mt. Papandayan, Indonesia
Bibliographic record
Abstract
During the past few years a methodology has been developed for processing data collected by GPS networks consisting of a mixed set of single-frequency and dual-frequency receivers. The strategy is to deploy a few permanent, 'fiducial' GPS stations with dual-frequency, geodetic-grade receivers surrounding an 'inner' network of low-cost, single-frequency GPS receivers. Such a configuration offers considerable flexibility and cost savings for geodynamic applications such as volcano deformation monitoring, which require a dense spatial coverage of GPS stations, and where it is not possible, nor appropriate, to establish permanent GPS networks using dual-frequency instrumentation. This configuration has recently been tested at the Mt. Papandayan volcano in West Java, Indonesia. The two-stage network design consists of an inner network of four single-frequency Canadian Marconi (CM) GPS receivers surrounded by three dual-frequency Leica CRS1000 GPS receivers. The inner network logged and transmitted GPS data from the 'slave' stations located on the volcano, to a base station. The combined processing of the CM and Leica receiver data was performed offline so as to investigate the performance of such a mixed-mode system. The basis of the processing methodology is to separate the dual-frequency, 'fiducial' station data processing from the baseline processing involving the single-frequency receivers on the volcano. The data processing for the former was carried out using a modified version of the Bernese software, to generate a file of 'corrections' (analogous to Wide Area DGPS correction models for the distance dependent biases -- primarily due to atmospheric refraction). These 'corrections' will then be applied to the double-differenced phase observations from the inner receivers to improve the baseline accuracies (primarily through empirical modelling of the residual atmospheric biases that otherwise would be neglected). A description of the field testing (and its challenges) during February-March 2000, together with a discussion of the results are presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
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 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".