Iron Rain: measuring the occurrence rate and origin of small iron meteoroids at Earth
Bibliographic record
Abstract
ABSTRACT We report results of a 4-yr survey using Electron Multiplied Charged Coupled Device cameras recording 34 761 two-station video meteor events complete to a limiting magnitude of +6. The survey goal was to characterize probable iron meteoroids. Using only physical properties of the meteor trajectories including early peaking light curves, short luminous trajectories, and high energies accumulated per area at beginning, we identified 1068 iron meteors. Our iron candidates are most abundant at slow speeds <15 km s−1, where they make up ≈20 per cent of the mm-sized meteoroid population. They are overwhelmingly on asteroidal orbits, and have particularly low orbital eccentricities and smaller semimajor axes when compared to non-irons between 10 and 20 km s−1. Our iron population appears to be more numerous at fainter magnitudes, comprising 15 per cent of slow (10–15 km s−1) meteors with peak brightness of +3 with the fraction rising to 25 per cent at +6 to +7, our survey limit. The iron orbits are most consistent with an asteroidal source and are in highly evolved orbits, suggesting long collisional lifetimes (107 yr). Metal-rich chondrules (nodules) found in abundance in EL chondrites are one possible source for this population. We also propose a possible technique using R-band colours to more robustly identify fainter iron meteors with very high confidence.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".