In-Situ Propellant Supplied Lunar Lander Concept
Bibliographic record
Abstract
Future NASA and commercial Lunar missions will require innovative spacecraft configurations incorporating reliable, sustainable propulsion, propellant storage, power and crew life support technologies that can evolve into long duration, partially autonomous systems that can be used to emplace and sustain the massive supplies required for a permanently occupied lunar base. Ambitious surface science missions will require efficient Lunar transfer systems to provide the consumables, science equipment, energy generation systems, habitation systems and crew provisions necessary for lengthy tours on the surface. Lunar lander descent and ascent stages become significantly more efficient when they can be refueled on the Lunar surface and operated numerous times. Landers enabled by Lunar In‐Situ Propellant Production (ISPP) facilities will greatly ease constraints on spacecraft mass and payload delivery capability, and may operate much more affordably (in the long term) then landers that are dependant on Earth supplied propellants. In this paper, a Lander concept that leverages ISPP is described and its performance is quantified. Landers, operating as sortie vehicles from Low Lunar Orbit, with efficiencies facilitated by ISPP will enable economical utilization and enhancements that will provide increasingly valuable science yields from Lunar Bases.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".