Modeling the received signal for the Canadian over-the-horizon-radar
Bibliographic record
Abstract
The detection performance of high frequency over-the-horizon radar (HF OTHR) systems is heavily influenced by the presence of radar clutter that originates by Bragg backscatter from plasma structures in the aurora zone, denoted as auroral clutter. The work in [1] developed a data model for the clutter seen by HF OTHR system. The model uses a geometric optics approach to determine the power spectral density (PSD) of the radar signal phase as a function of space and Doppler. However, in that approach the space-Doppler spectrum focuses on a single transmit-receive ray path. This, in turn, forces the clutter to occupy a very narrow notch in Doppler. This issue was addressed in [1] by multiplying the resulting clutter signal with a series of random Doppler shifts per pulse thereby introducing a Doppler spread in the clutter. In this paper we present an extension of this model for auroral clutter wherein the clutter is modelled as a superposition of multiple rays each of which, individually, satisfies the model in [1]. The paper also addresses the modelling of the target return that is not addressed in [1]. The target model presented here uses the power spectrum approach to estimate the angle-Doppler spread due to signal propagation through the ionosphere.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".