False Plot Identification Using Multi-frame Clustering for Compact HFSWR
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
Compact high-frequency surface wave radar suffers from a high false alarm rate in target detection due to its low transmit power and wide beam, thus a large number of false plots are produced, which increases the computational burden of subsequent target tracking algorithm and easily leads to producing false tracks. In this paper, a two-stage false plot identification method is proposed. Firstly, a multi-frame plot clustering algorithm is proposed to cluster the potential plots of the same target in several consecutive frames, the plots outside the clusters are removed as false plots. Then, the differences in terms of range and Doppler velocity between the plot in the center frame and those in its neighbor frames in each cluster are used as features. Finally, a trained extreme learning machine is applied to the obtained features to recognize the remaining false plots. Experimental results with both simulated and field data demonstrate the effectiveness of the proposed method for false plot identification.
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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) | 0.000 | 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 it