Improving Current Measurements from Wave Buoys: Results from a Successful Five-Year Collaborative Development Project
Bibliographic record
Abstract
Recognizing that detailed analyses of the accuracy of acoustic current profiles from instruments mounted on dynamic surface platforms (MetOcean or wave buoys) were lacking, the authors and colleagues began studying the issue in 2014. Initial testing with an ADCP mounted on a spherical wave buoy showed significant errors when compared with multiple bottom-mounted wave/current profilers. We initiated a repeated cycle of testing, analysis, reporting, and instrument improvement, with subsequent test deployments in 2015-2016, 2017-2018, and 2018-2019. Results from the first three of these have been reported in detail at previous OCEANS conferences and other technical workshops (IEEE CWTM, MTS Buoy Workshops) - see References [1]-[5] The process has led to evolutionary improvements to both the wave/current buoy design (AXYS TRIAXYS) and current profiler performance for buoy integration (NORTEK Signature AD2CP with AHRS). The resulting integrated platform was deployed under test conditions from December 2018 - March 2019. Preliminary comparisons show that the improvements have resulted in buoy-based current profiles that accurately match bottom-mounted profiles far better than previous versions (under the test conditions, 12-25 m depths, currents with significant vertical shear). Here we present results and analyses of the 2018-2019 deployments, which consisted of two TRIAXYS buoys (new design with central profiler mount and NORTEK Signature, an older offset profiler design with a NORTEK Aquadopp), and a nearby NORTEK AWAC current/wave profiler.
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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 | 0.018 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".