Computing optical meteor flux using global meteor network data
Bibliographic record
Abstract
ABSTRACT Meteor showers and their outbursts are the dominant source of meteoroid impact risk to spacecraft on short time-scales. Meteor shower prediction models depend on historical observations to produce accurate forecasts. However, the current lack of quality and persistent world-wide monitoring at optical meteoroid sizes has left some recent major outbursts poorly observed. A novel method of computing meteor shower flux is developed and applied to Global Meteor Network data. The method is verified against previously published observations of the Perseids and the Geminids. The complete mathematical and algorithmic details of computing meteor shower fluxes from video observations are described. As an example application of our approach, the flux measurements of the 2021 Perseid outburst, the 2020–2022 Quadrantids, and 2020–2021 Geminids are presented. The flux of the 2021 Perseids reached similar levels to the 1991–1994 and 2016 outbursts (ZHR ∼ 280). The flux of the Quadrantids shows high year-to-year variability in the core of the stream while the longer lasting background activity is less variable, consistent with an age difference between the two components. The Geminids show a double peak in flux near the time of peak.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.002 | 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".