Triple-frequency meteor radar full wave scattering
Bibliographic record
Abstract
Context. Radar scattering from meteor trails depends on several poorly constrained quantities, such as electron line density, q, initial trail radius, r0, and ambipolar diffusion coefficient, D. Aims. The goal is to apply a numerical model of full wave backscatter to triple frequency echo measurements to validate theory and constrain estimates of electron radial distribution, initial trail radius, and the ambipolar diffusion coefficient. Methods. A selection of 50 transversely polarized and 50 parallel polarized echoes with complete trajectory information were identified from simultaneous tri-frequency echoes recorded by the Canadian Meteor Orbit Radar. The amplitude-time profile of each echo was fit to our model using three different choices for the radial electron distribution assuming a Gaussian, parabolic-exponential, and 1-by-r2 electron line density model. The observations were manually fit by varying, q, r0, and D per model until all three synthetic echo-amplitude profiles at each frequency matched observation. Results. The Gaussian radial electron distribution was the most successful at fitting echo power profiles, followed by the 1∕r2. We were unable to fit any echoes using a profile where electron density varied from the trail axis as an exponential-parabolic distribution. While fewer than 5% of all examined echoes had self-consistent fits, the estimates of r0 and D as a function of height obtained were broadly similar to earlier studies, though with considerable scatter. Most meteor echoes are found to not be described well by the idealized full wave scattering model.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".