LANTR/ISPP-based space transportation for moon/Mars missions. I - Analysis
Bibliographic record
Abstract
The search for high-leverage propulsion technologies for human lunar and Mars missions lias turned up several nearand longer-term candidates. Among those expected to be available within ten years of beginning an advanced technology development program are the Nuclear Thermal Rocket (NTR) engine and In Situ Propellant Production (ISPP). Each of these concepts has considerable history in studies and experimentation. Some of our recent studies indicate even greater potential when the two technologies are combined. NASA lias proposed that a LOX-Augmented NTR (LANTR) small engine concept and tanks designed for use on lunar stages could also be used for Mars vehicle configurations, and that the tanks could be filled with propellants from the Moon, Phobos, or Mars as appropriate for the return trip. This approach preserves the strategy of using a few common design elements for both lunar and Mars missions, while also making a significant mass performance improvement for the Mars return stage. This paper describes the analysis used to evaluate the mission performance, cost, and transportation infrastructure implications of LANTR and ISPP for lunar and Mars missions. Current planning guidelines and assumptions are also documented. A companion paper [1] presents the results of this steady-state analysis of Earth-Moon and Earth-Mars in-space transportation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 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".