Development and Testing of a Multisensor Integrated Device for Reservoir Landslides Hydro-Fluctuation Zone
Bibliographic record
Abstract
The stability of reservoir landslides is significantly influenced by the hydraulic fluctuations in the hydro-fluctuation zone. However, integrated devices capable of simultaneously monitoring the deformation of landslides and the fluctuations of reservoir water levels in this zone are still unavailable. To address this, a multisensor integrated device (MSID) has been developed for real-time observation of the hydro-fluctuation zone of reservoir landslides in this study, incorporating functions such as cross-media ranging, landslide surface attitude alterations measurement, water level monitoring, and wave activity tracking. The radar sensor based on stepped-frequency continuous wave (SFCW) was adopted for the cross-media ranging across the air-water interface for the first time, and the corresponding computing method was also provided. Besides, a six-axis inertial measurement unit (IMU) and a piezoresistive level sensor were also integrated into the device for the monitoring of attitude alterations and changes in the reservoir hydrological environment. The test results show that the developed device is effective in ranging across the air-water interface, showing a high linear correlation between measured and actual distances under various water depths, with the best$R^{2}$being 0.9734 and a maximum range of 1.216 m at a water depth of 0.6 m. Also, the attitude alterations measurement demonstrates exceptional measurement precision for roll, pitch, and yaw angles with the best$R^{2}$infinity approaching 1. Furthermore, the device can accurately observe the water level as well as the frequency and amplitude of waves under both still water and wave conditions.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".