The Effects of Terrain Properties Upon the Small Crater Population Distribution at Giordano Bruno: Implications for Lunar Chronology
Bibliographic record
Abstract
area of Giordano Bruno's ejecta and compared the distribution of craters with variations in thermophysical properties derived from the Lunar Reconnaissance Orbiter Diviner instrument. We used Diviner-derived rock abundance and nighttime regolith temperatures along with thermal model-predicted surface temperatures for a diversity of terrains to identify and isolate areas of the ejecta based on thermophysical properties such as bulk density and thermal conductivity. We found that thermophysical properties of the ejecta vary considerably both laterally and vertically, and consistently differ from typical regolith, indicating the presence of higher thermal inertia materials. Crater-size frequencies are significantly lower in areas with terrain properties exhibiting higher: rock abundance, nighttime temperatures, and/or modeled thermal inertia. This discrepancy in crater distribution increases for craters smaller than ∼25 m. These thermophysical variations indicate changes in the mechanical properties of the target materials. We suggest that these variations-specifically, terrain-dependent crater scaling variations and impactor-scale heterogeneities in material properties such as the presence or absence of large boulders-may influence crater diameters or inhibit crater production altogether in Giordano Bruno's ejecta; furthermore, these factors are size-dependent.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".