Mass Estimation From Simultaneous Optical and Radar Meteor Observations
Bibliographic record
Abstract
Abstract The total mass flux due to meteoric input is not well constrained and estimates vary greatly depending on the measurement technique used. The source of this discrepancy remains an open question in the field. Previous studies investigating the discrepancy by directly comparing mass estimates made using two techniques have been limited by extremely small sample sizes. This work presents a set of 166 meteors observed simultaneously by the MAARSY radar (53.5 MHz) and two nearby optical cameras. Independent masses are estimated using observations from both systems and compared against each other. The resulting mass estimates using both methods agree to within a factor of three on average. The results show two dominant trends: better agreement as meteoroid velocity increases and underestimation of the radar mass for the largest meteoroids observed (>10 mg). These trends had not been quantified by previous studies limited by very small sample sizes, and could help to explain the historic discrepancy between mass estimates by different systems. The general agreement between the radar and photometric masses indicates that both methods perform well independently, and can reliably be applied to radar or optical observations without restriction of simultaneous observations by two systems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".