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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> 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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> 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.
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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".