Bibliographic record
Abstract
Abstract The self-replicating machine has high utility by virtue of its universal construction properties and its productive capacity for exponential growth. Their capacity is unrivalled. They can be deployed to the Moon to industrialize it using local in-situ resources in the short term to open up the solar system and thence deployed on interstellar spacecraft to explore the entire Galaxy by exploiting in-situ stellar system resources. Nevertheless, there are significant concerns regarding the inherent safety of self-replicating machines. We consider the general problem of runaway population growth in physical self-replicating machines to prevent the grey goo problem, the number of offspring spawned by self-replicating machines may be controlled at a genetic level. We adopt a biologically-inspired approach based on telomeres, DNA endcaps that are progressively shortened during cellular replication. This acts as a counter that imposes a limit to the number of replication cycles (Hayflick limit). By examining the biological process in detail, we can obtain some insights in implementing similar mechanisms in self-replicating machines. In particular, we find that counting mechanisms are vulnerable to cancerous runaway.
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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".