Bibliographic record
Abstract
To evolve or not to evolve? That is the question: whether 'tis nobler in the mind to suffer the slings and arrows of grey goo, or to deny evolution to a sea of self-replicators and by prevention control them? We have been developing a physical self-replicating machine concept for deployment on the Moon built from local resources on the Moon. Here, we are concerned with architectural issues - we specifically address the problem of uncontrolled replication. We propose a multitiered approach to prevent this: (i) denial of self-replication through the implementation of centralised mass manufacturing of replicators; (ii) denial of scarce sodium and chlorine from Earth acts as an Earth-controlled kill switch in preventing further replication; (iii) denial of centralised supplies of asteroidal metals (tungsten-nickel-cobalt-selenium) at the lunar south pole acts as a Moon-controlled kill switch; (iv) denial of online learning capacity through fixed neural weights; (v) denial of extended computing resources through the elimination of transmit communications between self-replicators; (vi) denial of evolutionary capacity by implementing error detection and correction (EDAC) coding. Two kill switches and EDAC provide the backbone to our approach that maintain self-replication capability.
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 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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".