Bibliographic record
Abstract
In our problem of identifying iceberg fragments in marine radar, we have previously applied Gabor’s expansion of a signal onto a set of Gaussian windowed sinusoids (Gabor functions). A somewhat sinusoidal signature in time-frequency space characterized the near circular movement of any floating object under the influence of ocean waves. Methods based on an adaptive version of this time-frequency processing have been shown[l] to track this sinusoidal nonstationarity and thus were very effective for detecting wave driven objects. An even better means of performing the detection, using “chirplets” was later developed[2]. The dynamics of the motion were modeled, first by a constant acceleration (expansion on windowed linear FM basis functions), then by an expansion onto a set of sinusoidal chirplets. (The sound of a police siren is a member of this set of bases). The sinusoidal chirplet model embodies the linear chirplet as a special case. Ordinary range-based processing assumes a piecewise stationary underlying model. The sliding window Doppler Fourier processing, assumes a more general underlying model, namely that of piecewise constant velocity. The linear chirplet generalizes further to constant acceleration. Finally the sinusoidal chirplet matches the physics’ of floating objects very closely and provides the best performance. Each one embodies the previous ones as special cases. We compare our new methods of Doppler processing with spatiotemporal processing[3]. Doppler processing is more suited to objects, such as icebergs, which remain within the same range cell for an extended period of time, while the spatiotemporal processing is more suitable for objects, such as ships, which move through multiple range cells.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".