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Record W2885575353 · doi:10.1061/9780784479971.066

How to Build a Self-Replicating Machine on the Moon

2016· article· en· W2885575353 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.

Bibliographic record

VenueEarth and Space 2016 · 2016
Typearticle
Languageen
FieldEngineering
TopicModular Robots and Swarm Intelligence
Canadian institutionsCarleton University
Fundersnot available
KeywordsMechatronicsComputer scienceReplication (statistics)KinematicsElectronicsMechanism (biology)Electric motorDistributed computingControl engineeringArtificial intelligenceMechanical engineeringEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

The ultimate product from extraterrestrial in-situ resource utilization is a self-replicating machine by virtue of its ability to exponentially expand productive capacity. Self-replication is a byproduct of the universal constructor and we propose that 3D printing technology constitutes a universal construction mechanism. Universal construction is based on the manufacture of robotic mechanisms that are the core to any motorized kinematic machine. All robotic mechanisms are mechatronic systems comprising motors, sensors and control system. We present preliminary work to demonstrate that electric motors and electronics (as the medium of control) can be 3D printed. We also impose the constraint that our self-replicating machine be constructed entirely from lunar-derived materials. The acquisition of these materials is emphasized. The existence of self-replication capacity on the Moon would revolutionise space exploration and introduce the possibility of robust space-based solutions to our current global environmental problems at low cost.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.846
Threshold uncertainty score0.284

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 head0.008
GPT teacher head0.192
Teacher spread0.183 · 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