Development of a Fully Automated, GPS Based Monitoring System for Disaster Prevention and Emergency Preparedness: PPMS+RT
Bibliographic record
Abstract
The increasing number of structural collapses, slope failures and other naturaldisasters has lead to a demand for new sensors, sensor integration techniques and dataprocessing strategies for deformation monitoring systems. In order to meet extraordinaryaccuracy requirements for displacement detection in recent deformation monitoringprojects, research has been devoted to integrating Global Positioning System (GPS) as amonitoring sensor. Although GPS has been used for monitoring purposes worldwide,certain environments pose challenges where conventional processing techniques cannotprovide the required accuracy with sufficient update frequency. Described is thedevelopment of a fully automated, continuous, real-time monitoring system that employsGPS sensors and pseudolite technology to meet these requirements in such environments.Ethernet and/or serial port communication techniques are used to transfer data betweenGPS receivers at target points and a central processing computer. The data can beprocessed locally or remotely based upon client needs. A test was conducted that illustrateda 10 mm displacement was remotely detected at a target point using the designed system.This information could then be used to signal an alarm if conditions are deemed to beunsafe.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".