Performance of<i>D</i>-criteria in isolating meteor showers from the sporadic background in an optical data set
Bibliographic record
Abstract
Separating meteor showers from the sporadic meteor background is critical for the study of both showers and the sporadic complex. The linkage of meteors to meteor showers, to parent bodies, and to other meteors is done using measures of orbital similarity. These measures often take the form of so-called D-parameters and are generally paired with some cutoff value within which two orbits are considered related. The appropriate cut-off value can depend on the size of the data set (Southworth & Hawkins 1963), the sporadic contribution within the observed size range (Jopek 1995), or the inclination of the shower (Galligan 2001). If the goal is to minimize sporadic contamination of the extracted shower, the cut-off value should also reflect the strength of the shower compared to the local sporadic background. In this paper, we present a method for determining, on a per-shower basis, the orbital similarity cut-off value that corresponds to a chosen acceptable false-positive rate. This method also assists us in distinguishing which showers are significant within a set of data. We apply these methods to optical meteor observations from the NASA All-Sky and Southern Ontario Meteor Networks.
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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 | 0.036 | 0.079 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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".