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Record W3183792198 · doi:10.1139/cjce-2021-0098

Leveraging in situ resources for lunar base construction

2021· article· en· W3183792198 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsCarleton University
Fundersnot available
KeywordsBase (topology)Resource (disambiguation)Range (aeronautics)Mars Exploration ProgramMining engineeringEnvironmental scienceCivil engineeringEngineeringComputer scienceAstrobiologyAerospace engineering

Abstract

fetched live from OpenAlex

We explore the limits of in situ resource utilization (ISRU) on the Moon to maximize living off the land by building lunar bases from in situ material. We adopt the philosophy of indigenous peoples who excelled in sustainability. We are interested in leveraging lunar resources to manufacture an entire lunar base in such a fashion that is fully sustainable and minimizes supplies required from Earth. A range of metals, ceramics and volatiles can be extracted from lunar minerals to support construction of a lunar base that include structure, piping and electrical distribution systems. To 3D print a lunar base, we must 3D print the load-bearing structure, electrical distribution system, water-based heating system, drinking water system, air system and orbital transport system from in situ resources. We also address the manufacture of the interior of the lunar base from local resources. The majority of systems constituting a lunar base can be manufactured from in situ resources.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.517
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.174
Teacher spread0.165 · 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