The Global Meteor Network – Methodology and first results
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT The Global Meteor Network (GMN) utilizes highly sensitive low-cost CMOS video cameras which run open-source meteor detection software on Raspberry Pi computers. Currently, over 450 GMN cameras in 30 countries are deployed. The main goal of the network is to provide long-term characterization of the radiants, flux, and size distribution of annual meteor showers and outbursts in the optical meteor mass range. The rapid 24-h publication cycle the orbital data will enhance the public situational awareness of the near-Earth meteoroid environment. The GMN also aims to increase the number of instrumentally observed meteorite falls and the transparency of data reduction methods. A novel astrometry calibration method is presented which allows decoupling of the camera pointing from the distortion, and is used for frequent pointing calibrations through the night. Using wide-field cameras (88° × 48°) with a limiting stellar magnitude of +6.0 ± 0.5 at 25 frames per second, over 220 000 precise meteoroid orbits were collected since 2018 December until 2021 June. The median radiant precision of all computed trajectories is 0.47°, 0.32° for $\sim 20{{\ \rm per\ cent}}$ of meteors which were observed from 4 + stations, a precision sufficient to measure physical dispersions of meteor showers. All non-daytime annual established meteor showers were observed during that time, including five outbursts. An analysis of a meteorite-dropping fireball is presented which showed visible wake, fragmentation details, and several discernible fragments. It had spatial trajectory fit errors of only ∼40 m, which translated into the estimated radiant and velocity errors of 3 arcmin and tens of meters per second.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it