Meteorites from meteor showers: A case study of the Taurids
Bibliographic record
Abstract
Abstract We propose that the Taurid meteor shower may contain bodies able to survive and be recovered as meteorites. We review the expected properties of meteorite‐producing fireballs, and suggest that end heights below 35 km and terminal speeds below 10 km s−1 are necessary conditions for fireballs expected to produce meteorites. Applying the meteoroid strength index (PE criteria) of Ceplecha and McCrosky (1976) to a suite of 33 photographically recorded Taurid fireballs, we find a large spread in the apparent meteoroid strengths within the stream, including some very strong meteoroids. We also examine in detail the flight behavior of a Taurid fireball (SOMN 101031) and show that it has the potential to be a (small) meteorite‐producing event. Similarly, photographic observations of a bright, potential Taurid fireball recorded in November of 1995 in Spain show that it also had meteorite‐producing characteristics, despite a very high entry velocity (33 km s−1). Finally, we note that the recent Maribo meteorite fall may have had a very high entry velocity (28 km s−1), further suggesting that survival of meteorites at Taurid‐like velocities is possible. Application of a numerical entry model also shows plausible survival of meteorites at Taurid‐like velocities, provided the initial meteoroids are fairly strong and large, both of which are characteristics found in the Taurid stream.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".