Bibliographic record
Abstract
The TRIAXYS Directional Wave Sensor has been a leading technology for directional wave measurements since its inception in the late 1990's. Many technical product improvements have been made over the years related to the onboard micro-processor and the inertial sensor to capture raw motion data, to reduce the physical size and to lower power draw. This paper will provide a brief history of the technology evolution the TRIAXYS wave sensor has undergone, from its original release in 1998 through each of the incremental improvements to the most recent technical advances in the next generation of the sensor the TRIAXYS g3. The discussion on the TRIAXYS g3 will include: 1. User configurable frequency partitioning in real-time: wave analysis performed on total spectrum plus two sub-ranges within total spectrum 2. Refactored messages: reduced bandwidth for real time data using Protocol Buffers, user selectable wave statistic ranges and directional spectra 3. Continuous wave analysis and processing output: rolling sample period with data sent every minute 4. Significant reductions in physical size and power consumption. Results from validation studies conducted off the West Coast of Vancouver Island and the German North Sea are presented comparing the wave measurement performance of the TRIAXYS Next Wave II and TRIAXYS g3 sensors co-located in a single TRIAXYS directional wave buoy. A comparison of the wave statistics and energy spectra, between the two sensors, will be discussed. This report outlines how wave sensor technology has advanced over the last 17 years, how the TRIAXYS g3 and TRIAXYS Next Wave II sensors perform comparatively, and how users can benefit from the recent technical improvements to receive comprehensive information on ocean waves for various studies and requirements.
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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) | 0.006 | 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; both teacher heads agree on what is shown here.
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".