The capabilities of Doppler current profilers for directional wave measurements in coastal and nearshore waters
Bibliographic record
Abstract
The adaptation of Doppler current profilers to measure directional wave spectra has provided a new instrumentation approach to coastal and nearshore oceanographic studies. Past studies have shown favorable comparisons between Doppler current profiler wave instruments with bottom mounted PUV (pressure-velocity) sensors sampling at wave frequencies and wave buoys. In this paper, we examine the capabilities and limitations of two different Doppler current profilers for directional wave measurements in shallow coastal waters of 0-25 m water depth. Data collection programs using Doppler current profilers for wave measurements have been conducted for one month long periods in the early spring of 2002, 2003 and 2004 on Roberts Bank in the Fraser River foreslope region of the Strait of Georgia, British Columbia, Canada. In 2004, an RD Instrument ADCP along with the newly-released 1000 kHz Nortek AWAC current profiler and wave instrument were co-located in 7 m water depth at a different site on the edge of Roberts Bank. Inter-comparisons between these bottom mounted instruments are used to examine the capabilities of the directional wave spectral parameters, in terms of: resolvable frequencies for directional and nondirectional wave spectra; wave directional resolution and reliability, and limitations arising from the use of linear wave theory. For a preliminary assessment of the capability of Doppler wave spectra in deeper waters of 20-25 m depths, in particular for very long wave periods, some experiences derived from a long-term measurement program being conducted off the west coast of Africa are presented
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".