An Algorithm to Separate Ionospheric Turbulence Radar Echoes From Those of Meteor Trails in Large Data Sets
Bibliographic record
Abstract
Abstract Coherent scatter echoes from disintegrating meteors and from the unstable ionospheric E‐region can overlap considerably between 90 and 110 km altitudes. As the physical origin of plasma irregularities produced by meteor trails differs starkly from that of E‐region auroral irregularities, this has consequences for winds as well as electrodynamic studies, thereby introducing a need to distinguish between the two types of echoes. To that goal, we have developed a novel separation algorithm to automatically sort through arbitrarily large data sets in the region of overlap. This proves very useful when the 3D location of echoes is available. The algorithm uses a definition of crowding, or clustering, in both time and space and has been developed and tested with a comprehensive data set obtained from the recently built Canadian icebear 3D radar. We discuss the characteristics belonging to the two classes of echoes, and present statistical results about the location of each type of echo as a function of conditions. Our proposed algorithm can be applied to any coherent scatter echo data with high resolution 3D location information.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".