Detecting meteoroids with the Southern Argentina Agile Meteor Radar Orbital System (SAAMER-OS): applications for atmospheric and astronomical research.
Bibliographic record
Abstract
Ground-based meteor radars detect the plasma streaks produced when meteoroids ablate in our atmosphere. However they are limited to detecting particles that produce a sufficient amount of plasma within the instrument’s field-of-view, and thus most of the meteoroid’s trajectory remains undetected. Previous work by Dawkins et al. (2023) and Stober et al. (2023) utilised new polarisation measurements made by the Southern Argentina Agile Meteor Radar Orbital System (SAAMER-OS, 53.8oS, 67.8oW, Janches et al., 2019), in conjunction with two state-of-the-art models, in order to determine the pre-atmosphere dynamical characteristics (mass, velocity) of the detected particles before they suffered any significant ablation or deceleration. Subsequent work has focused on automating this methodology, to allow us to determine the pre-atmosphere characteristics for all meteoric particles detected by SAAMER-OS. In this work we describe this background methodology and how it can be applied to different facets of atmospheric and astronomical research, including (1) how we can characterise the astronomical sources detected at SAAMER-OS through time (mass and velocities), (2) detections of new meteor showers, (3) to understand the mass distribution function of particles that enter the top of the atmosphere, and (4) variability of atmospheric neutral densities in the Earth’s upper atmosphere.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".