Thermophysical Properties of Lunar Irregular Mare Patches From LRO Diviner Radiometer Data
Bibliographic record
Abstract
Abstract Irregular mare patches (IMPs) are enigmatic features in the lunar maria that are characterized as having smooth mounds surrounded by uneven or blocky terrain. The IMPs appear anomalously young compared to surrounding maria, with crater counting methods estimating IMPs to be <100 Myr old and established measures of maturity showing that IMPs are relatively immature. Some studies propose that IMPs are the result of recent basaltic volcanism or episodic outgassing of volatiles, while others propose that the IMPs are of a similar age to the surrounding maria and appear geologically young due to unique physical properties resulting from late‐stage eruption dynamics. Here we present observations from the Lunar Reconnaissance Orbiter Diviner Lunar Radiometer Experiment for eight of the largest IMPs. We find that IMPs are slightly rockier than typical mare surfaces, and the fines components of a number of IMPs have relatively low thermal inertia. The IMP Ina has noticeably lower thermal inertia than the other IMPs. We suggest that the other IMPs (which in many cases are smaller and have ambiguous or gradational contacts with the surrounding maria) could contain similarly low thermal inertia material as Ina, but that it is not fully resolvable with Diviner. We interpret lower‐thermal inertia material as being less‐consolidated or containing fewer small rocks than typical regolith. This would be consistent with elements of a number of proposed formation hypotheses; however, higher spatial resolution is needed for a full comparison of the thermophysical properties of the different IMPs and different terrains within each IMP.
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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 | 0.000 | 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) | 0.002 | 0.001 |
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".