Introduction and Testing of a Cost-Effective GNSS System for Landslide Monitoring
Bibliographic record
Abstract
Abstract The use of Global Navigation Satellite System (GNSS) in combination with real-time kinematic (RTK) technique, commonly known as differential GNSS (dGNSS), has increased in recent years for monitoring landslide displacements and detecting early signs of potential failure, enabling earlier response for risk mitigation than traditional monitoring techniques. GNSS offers several advantages, including high accuracy and high-frequency data collection. Although more cost-effective, their affordability may still present challenges for public organizations managing multiple landslides in their territory. The SparkFun is a suite of components for GNSS assembly designed for topographic surveying, offering the benefits of dGNSS technology while being more affordable than other market options. It also avoids relying on phone signals for data storage in a cloud server. The SparkFun system, its components, and how it can be assembled to create a dGNSS system for landslide monitoring are described in this paper. The deployment and testing of a SparkFun system at the Chin Coulee landslide in Alberta, including challenges faced during the 6-month period, are described. The results are compared with those from a commercially available dGNSS system (Ophelia Geocube) developed for landslide monitoring in the area. Preliminary findings show that the SparkFun system demonstrates horizontal accuracy consistent with the manufacturer's specifications and exhibits displacement trends comparable to the 2018 Geocube monitoring campaign. The robustness of the power supply system and environmental insulation of the equipment needs to be enhanced for future deployments. The SparkFun assembly is shared in this paper, for others to test and deploy their own prototypes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".