Mass Loss Due to Sputtering and Thermal Processes in Meteoroid Ablation
Bibliographic record
Abstract
Conventional meteoroid theory assumes that the dominant mode of ablation is by evaporation following intense heating during atmospheric flight. In this paper we consider the question of whether sputtering may provide an alternative disintegration process of some importance.For meteoroids in the mass range from 10^-3 to 10^-13 kg and covering a meteor velocity range from 11 to 71 km/s, we numerically modeled both thermal ablation and sputtering ablation during atmospheric flight. We considered three meteoroid models believed to be representative of asteroidal (3300 kg m^-3 mass density), cometary (1000 kg m^-3) and porous cometary (300 kg m^-3) meteoroid structures. Atmospheric profiles which considered the molecular compositions at different heights were used in the sputtering calculations. We find that while in many cases (particularly at low velocities and for relatively large meteoroid masses) sputtering contributes only a small amount of mass loss during atmospheric flight, in some cases sputtering is very important. For example, a 10^-10 kg porous meteoroid at 40 km/s will lose nearly 51% of its mass by sputtering, while a 10^-13 kg asteroidal meteoroid at 60 km/s will lose nearly 83% of its mass by sputtering. We argue that sputtering may explain the light production observed at very great heights in some Leonid meteors. The impact of this work will be most dramatic for very small meteoroids such as those observed with large aperture radars.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".