Comparison of Spectral Estimation Methods for Rapidly Varying Currents Obtained by High-Frequency Radar
Bibliographic record
Abstract
A comparative study of the periodogram method and high-resolution techniques (the autoregressive and multiple signal classification methods) for current mapping by a high-frequency (HF) surface wave radar is undertaken for the case of 66-s-long data. This analysis is extended from a previous study that used the commonly adopted 6-13-min coherent integration times. This reduction in the sample size will result in poor Doppler resolution and reduction in signal-to-noise ratio (SNR) for the conventional periodogram method. Two Bragg-peak identification methods for current estimation, the conventional centroid method and the symmetric-peak-sum (SPS) method, are examined in conjunction with each of the spectral estimation techniques. A weighted sum of the current estimates using the two Doppler shift identification methods is also recommended to provide a lower root mean square (RMS) difference. The weight is optimized using a genetic algorithm. Field data comparison with current measurements obtained from a current meter indicates that the high-resolution spectral estimation method is capable of providing the same RMS difference level for short and long time series, while the RMS difference for currents obtained from the periodogram method increases dramatically for short time series. Significant improvement in the current velocities retrieved from a short time series indicates the potential for measuring rapidly changing currents using the suggested technique.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".