Measuring Ocean Surface Waves
Bibliographic record
Abstract
Propagating waves on the surface of the ocean can be represented as a stochastic process, whose statistics are characterized by a spectrum. Measuring the wave spectrum, and quantities derived from the spectrum, are reviewed here. Observation begins by sensing some property of the sea surface over space and/or time. Visual observations, collected routinely since the mid 18th century, comprise the longerest running wave record. Measurement methods in the nearshore are advancing, including traditional methods using pressure and acoustic sensing, but also newer methods such as distributed acoustic sensing and lidar. Detailed, small scale wave physics can now be explored with measurement techniques using light, including stereo-imaging and polarimatry. The decrease in size, cost, and power consumption of microelectronics has propagated through to ocean wave instrumentation, most notably in wave buoys. Global networks of freely drifting miniature wave buoys offer novel observational power. Remote sensing techniques based on radar and lidar continue to evolve, and are widely deployed from land and on ships, aircraft, autonomous vehicles, and satellites. Spaceborne altimeters form one of the most important records of wave height, and a suite of suite of new spaceborne sensors are observing directional spectra across the globe with sampling akin to traditional altimetry. Aircraft and autonomous systems are providing strategic sampling capabilities, whether for detailed process studies or accessing extreme storm environments. The quality and quantity of ocean wave measurements has never been greater. This review will help you make sense of it all.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".