Volcanic Processes in the Gassendi Region of the Moon
Bibliographic record
Abstract
Abstract The lunar floor‐fractured crater Gassendi and surrounding area were examined with high‐resolution Lunar Reconnaissance Orbiter imagery and other remote sensing data to characterize and understand the volcanic processes in the southwestern region of the Moon. This study was selected because the Gassendi region exhibits a variety of volcanic features (e.g., cryptomaria deposits, pyroclastic deposits, maria, and lava lakes) and team participants have studied this region for 30 years (Hawke et al., 1991, https://doi.org/10.1029/92GL02921 ). This study confirms the existence of a previously identified cryptomare deposit and identifies an additional cryptomare deposit west of Gassendi crater and a pyroclastic northeast of Gassendi. Spectral and geochemical anomalies associated with dark‐haloed impact craters reveal cryptomaria deposits in the western Gassendi crater floor and previously unmapped mare basalt within northeastern Gassendi. We identified three separate lava lakes on the northeast, northwest, and southwest floor of Gassendi crater based on morphology analogous to terrestrial lava lakes, geochemical signatures, and digital terrain data. Crater count (model) age data suggest that the lava lakes were active at ~3.6 Ga (300 Ma after floor emplacement). Criteria used to identify lava lakes in Gassendi were applied globally to locate candidate lava lakes within floor‐fractured craters. With the identification of lava lake morphology, both in Gassendi crater and in other floor‐fractured craters, the current ascent and eruption models should be revised to allow for at least short‐term connection between magma supply at depth and surface lava lakes. Hence, this integration of multiple perspectives afforded by recent remote data sets reveals new views about lunar volcanic processes.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".