Current bombardment of the Earth-Moon system: Emphasis on cratering asymmetries
Bibliographic record
Abstract
We calculate the current spatial distribution of projectile delivery to the Earth and Moon using numerical orbital dynamics simulations of candidate impactors drawn from a debiased Near-Earth-Object (NEO) model. Surprisingly, we find that the average lunar impact velocity is 20 km/s, which has ramifications for converting observed crater densities to impactor size distributions. We determine that current crater production on the leading hemisphere of the Moon is 1.29 +/- 0.01 that of the trailing when considering the ratio of craters within 30 degrees of the apex to those within 30 degrees of the antapex and that there is virtually no nearside-farside asymmetry. As expected, the degree of leading-trailing asymmetry increases when the Moon's orbital distance is decreased. We examine the latitude distribution of impactor sites and find that for both the Earth and Moon there is a small deficiency of time-averaged impact rates at the poles. The ratio between deliveries within 30 degrees of the pole to that of a 30 degree band centered on the equator is nearly unity for Earth (<1%)(0.992 +/- 0.001) but detectably non-uniform for the Moon (~10%)(0.912 +/- 0.004). The terrestrial arrival results are examined to determine the degree of AM/PM asymmetry to compare with meteorite fall times (of which there seems to be a PM excess). Our results show that the impact flux of objects derived from the NEOs in the AM hours is ~2 times that of the PM hemisphere, further supporting the assertion that meteorite-dropping objects are recent ejections from the main asteroid belt rather than young fragments of NEOs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".