Properties of marine radar imagery and directional spectra of the sea surface
Bibliographic record
Abstract
In the last decade a number of researchers have published "directional wave spectra" calculated from marine radar images. The author's group has continued this line of research using a unique radar system that digitizes and stores radar images eight bits deep directly related to the strength of the radar backscatter. The author collected in excess of 3000 sea surface backscatter images during the Grand Banks ERS-1 SAR Wave Spectrum Validation Experiment cruise in November, 1991. This image set demonstrates new features of microwave reflection at grazing incidence. The images show maximum backscatter intensity in the direction of the local wind, which is not necessarily in the direction of the dominant waves. The fall-off in intensity with distance appears to be relatively independent of both wind speed and wave conditions. From these images the author has produced a number of directional spectra. Spectral peaks show a strong azimuthal asymmetry unrelated to the wind direction. Both of these asymmetries must be considered in the creation of an unbiased spectrum. The spectra compare favorably with those calculated from directional wave buoy data, satellite synthetic aperture radars and other directional wave measurements and models.>
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 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".