Framework for Coordinated Efforts in the Exploration of Volatiles in the South Polar Region of the Moon
Bibliographic record
Abstract
Abstract The exploration of the lunar south polar region and the ground truthing of polar volatiles is one of the top priorities for several space agencies and private partners. Here we use Moon Mineralogy Mapper surficial water ice detections to investigate the location of water-ice-bearing permanently shaded regions (PSRs) near the south pole. We extract a variety of parameters such as their temperature regime, slope, hydrogen content, number of ice detections, depth stability for water ice and dry ice, and mobility aspects. We identify 169 water-ice-bearing PSRs and use their characteristics to identify sites that allow us to access the highest abundances of volatiles, sites that can be visited to characterize the lateral or vertical distribution of volatiles (water ice and dry ice), and sites that allow for the fastest recovery of a scientifically interesting sample. Collectively, 37 PSRs are identified as sites of interest, including 11 that would address more than one mission objective and may be, for that reason, higher-priority targets of exploration. These PSRs are found in Shoemaker, Faustini, Cabeus, Malapert, Nobile, Sverdrup, Wiechert J, and Haworth craters, as well as three unnamed craters (PSRs 57, 120, and 89). These sites are all located within 6° of the south pole. We present case studies for a relatively short traverse mission (20–50 km) to PSR 89, a medium-length traverse (∼100 km) to Sverdrup 1, and a longer traverse (∼300 km) to Cabeus that can serve as a guide in planning upcoming exploration missions.
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 | 0.033 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".