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Record W4313509477 · doi:10.1061/9780784484470.072

Lunar Base Construction Planning

2023· article· en· W4313509477 on OpenAlexaboutno aff
Robert P. Mueller

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTechnology Assessment and Management
Canadian institutionsnot available
Fundersnot available
KeywordsCrewInternational Space StationAeronauticsProcurementHuman spaceflightMission control centerSpace explorationEngineeringComputer scienceBusinessAerospace engineering

Abstract

fetched live from OpenAlex

Previous lunar missions and campaigns have been restricted to using robotic landers and lunar orbiting satellites as well as sortie type of operations using astronaut crews (NASA Apollo program). Now, the next phase of lunar exploration has begun under NASA’s Artemis program and there has been an international response where other nations such as China, Russia, India, Canada, Japan, and the European Union of nations, have all expressed interest in either collaborating or competing with NASA on the Moon. This next phase has an overarching goal of achieving a permanent human presence on the Moon via sustainable methods. A lunar base with human occupancy will require infrastructure to provide shelter, utilities, landing/launch pads, roads, communications, power, and all the other necessities to sustain human life and protect equipment. Since human biology is not well suited for surviving in the lunar environment, there will be many forms of automated equipment, autonomy, and robotic helpers that will minimize the amount of extra-vehicular activity (EVA) required by the crew. This will mean that the radiation dosage received by the crew will stay within acceptable and safe career doses. Radiation shielding via the use of regolith can also mitigate radiation dangers. The required infrastructure must be constructed, but the mass and logistics of bringing all the construction materials from Earth are prohibitive, which makes the necessary construction difficult to achieve. In situ resource utilization (ISRU) aims to solve this challenge by sourcing construction materials locally or “in situ.” This means that their transportation can be completely eliminated, resulting in large cost savings by avoiding the launch out of Earth’s deep gravity well and subsequent trans lunar injection, lunar orbit capture, and landing. This paper will give a historical review and current status of lunar construction planning and a high level introduction to the required infrastructure and construction equipment that will be required to robotically build a lunar base using in situ resources. It will also organize these tasks into logical groupings so that technology development and implementation can be pursued within a framework that can be referenced by all involved.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.068
Threshold uncertainty score0.228

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0020.000
Scholarly communication0.0030.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0680.021

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.

Opus teacher head0.012
GPT teacher head0.229
Teacher spread0.217 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2023
Admission routes1
Has abstractyes

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