Bibliographic record
Abstract
To reexamine the potential for lateral mixing over large distances (>100 km) by impact craters, a mathematical model utilizing the stable probability distribution is proposed for estimating lateral mixing efficiency on the Moon. The proposed model divides material mixing into shallow slope and steep slope regimes. Mixing in the shallow slope regime conforms to the condition that the exponent of the power law describing lunar crater size frequency is larger than the exponent of the power law describing the rim thickness plus 2; otherwise the mixing is called the steep slope regime. The model suggests that in the shallow slope regime, lateral mixing on the Moon is efficient enough to deliver 20–30% exotic components over distances greater than 100 km (e.g., highland material to the mare). The model indicates that lateral mixing conforming to the steep slope regime is not efficient if linear addition of ejecta deposits is assumed because in this regime, impact cratering is driven by small craters reworking lunar surface, and the addition of crater ejecta is an invalid assumption. If the result of the proposed model for the shallow slope regime is applied to regolith layers below the reworked zone, a significant number of “exotic” components is predicted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".