Meteor shower detection with density‐based clustering
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 We present a new method to detect meteor showers using the density‐based spatial clustering of applications with noise algorithm ( DBSCAN ; Ester et al. ). The DBSCAN algorithm is a modern cluster detection algorithm that is well suited to the problem of extracting meteor showers from all‐sky camera data because of its ability to efficiently extract clusters of different shapes and sizes from large data sets. We apply this shower detection algorithm on a data set that contains 25,885 meteor trajectories and orbits obtained from the NASA All‐Sky Fireball Network and the Southern Ontario Meteor Network ( SOMN ). Using a distance metric based on solar longitude, geocentric velocity, and Sun‐centered ecliptic radiant, we find 25 strong cluster detections and six weak detections in the data, all of which are good matches to known showers. We include measurement errors in our analysis to quantify the reliability of cluster occurrence and the probability that each meteor belongs to a given cluster. We validate our method through false‐positive/negative analysis and with a comparison to an established shower detection algorithm.
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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 | 0.000 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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