Parametric texture estimation and prediction using measured sea clutter data
Bibliographic record
Abstract
In this study, the authors present a deterministic parametric sea clutter texture model for high‐resolution radar backscatter at low‐grazing angles in the open ocean. The clutter texture forms a component of the compound‐Gaussian sea clutter model and they exploit the spatiotemporal relationships in the clutter by relating it to its physical source: sea swell. They present an efficient algorithm for the estimation of the spectral components for the parametric texture model through the estimation of two‐dimensional (2D) ‘tones’ across contiguous range bins instead of a series of 1D estimates as is used elsewhere. Validation is performed by comparing the predictive fit for their estimator with a series of temporal estimators and a non‐parametric estimator using measured sea clutter data from the Atlantic Ocean recorded by the Intelligent PIXel (IPIX) radar of McMaster University in Canada. Implementation of the spatiotemporal estimator results in a more parsimonious estimate with improved reliability due to the increased separation of the tones in 2D space. Their results are shown to agree with an established physical model for the sea swell.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".