The Subsurface Coherent Rock Content of the Moon as Revealed by Cold‐Spot Craters
Bibliographic record
Abstract
Abstract A recently identified class of young lunar craters, cold‐spot craters, provides an optimal data set for probing the subsurface rock content on the Moon. Rocks, or lack of rocks, in crater ejecta have long been used as indicators of regolith thickness. However, the rockiness of crater ejecta depends on the subsurface rock content, crater excavation depth, and crater age. Cold‐spot craters are surrounded by a low thermal inertia signature that fades within 1 Myr, so any rocks in the proximal ejecta blanket have not yet been broken down or buried. Thus, the rock abundance in the proximal ejecta blankets of cold‐spot craters is affected only by the subsurface rock content of the target and the crater excavation depth, not the age of the crater. We show that an abrupt transition between fine‐grained regolith and underlying coherent rock cannot explain the observed rock abundance. Instead, we assume that the volume fraction of coherent rock (in contrast to fine‐grained regolith) increases exponentially in the lunar subsurface and use the observed Diviner rock abundance in cold‐spot crater ejecta to solve for the “e‐folding” depth over which the volume fraction of rock increases. In general, the subsurface rock content is higher in the maria than in the highlands consistent with more recent resurfacing of the maria. However, the highlands show a wider range of subsurface rock content values overall including some overlapping those of the maria. Some of this variability appears to be associated with the Orientale basin and possibly with several cryptomare deposits.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".