Evaluation of a GNSS Buoy With Real-Time Precise Point Positioning Ability for Monitoring Tides and Ocean Waves
Bibliographic record
Abstract
Considerable demand exists for precise sea surface height data to assess tide level, ocean wave height, tsunami height, and sea level in offshore regions. However, the limited availability of structures for installing water level sensors necessitates the adoption of innovative observation technologies in these areas. This study developed a global navigation satellite system (GNSS) buoy with real-time precise point positioning (RT-PPP) capability to monitor sea levels in nearshore and offshore areas. The TerraStar-C PRO service (Novatel Inc., Calgary, AB, Canada) was employed for RT-PPP. A static laboratory test indicated that the standard deviation for all measured height data was 0.016 m. Moreover, in a kinematic laboratory test, the wave height error was −0.2%. Thus, the RT-PPP performance was satisfactory. A field test indicated that the tide data measured by the developed GNSS buoy closely matched those recorded at a nearby tide station, with the root mean square error between the two sets of data being 0.074 m. Additionally, the significant wave heights obtained from GNSS were refined using a linear regression method. The significant wave heights, mean wave periods, peak wave periods, and dominant wave directions obtained from the GNSS sensor were generally consistent with those acquired from an accelerometer–tilt–compass wave sensor, with the root mean square errors in these parameters being 0.11 m, 0.13 s, 0.63 s, and 10.0°, respectively. In summary, the results of this study indicate that the developed GNSS buoy with RT-PPP ability is a viable tool for monitoring tides and ocean waves in nearshore and offshore areas where real-time kinematic ambiguity resolution is unreliable.
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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 | 0.001 | 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".