A new algorithm to separate meteor trail echoes from ionospheric radar scatter
Bibliographic record
Abstract
Coherent scatter echoes from meteors entering Earth’s atmosphere and those from the ionospheric E-region overlap: echoes of both types are seen at altitudes between 95 km -105 km. The physical origin of plasma irregularities produced by disintegrating meteors naturally differ from that of ionospheric turbulence, and there is a need to distinguish between the two types of echoes. We present a novel algorithm to automatically sort through arbitrarily large datasets of radar echoes with accurate location data, classifying each echo as either meteoric or ionospheric in origin. The algorithm establishes a definition of clustering, in both time and space. We use data from ICEBEAR 3D, an experimental coherent scatter radar in Saskatchewan, Canada. We discuss the two classes of scatter echoes, and present statistical results from 2020, 2021. In future experiments, our proposed algorithm can be applied to both coherent and incoherent radar scatter, provided they come with 3D location information.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".