Adaptive CFAR active sonar signal thresholding using radial basis functional neural networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A recursive version of the adaptive constant false alarm rate (CFAR) sonar signal thresholding scheme using radial basis functional neural networks is proposed. Intensity thresholding has proven to be an effective technique to eliminate the low energy noise and to reduce the computational load in an underwater target tracking system. The proposed system has the following advantages: 1) the technique yields unbiased estimates under a nonhomogenous sea environment, because the false alarm rate is maintained at a constant level while the threshold changes with different sea environments; 2) the threshold for different range cells can be adaptively estimated since the noise under estimation is strictly local so that the received intensities of noise and targets are not affected by the distance the sonar signals travelled; and 3) the computational requirements are greatly reduced through the introduction of the recursive scheme.
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.
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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it