Bibliographic record
Abstract
John Conway’s Game of Life, published in Scientific American in 1970 is an attempt to model the behavior of life using a 2D cellular automaton. Although a breakthrough discovery for cellular automata and emergence theory, the game is restricted and incomplete due to its static, simplified rules. We will show that the game does not model life accurately and propose an alternative: TrueLife. TrueLife is a non deterministic, nonlocal, evolving Game of Life variant that we believe is more complete than Life for several key reasons. TrueLife is unique since at each generation a rule is chosen randomly from a list and applied to the current state. This allows the game to be inherently nondeterministic since it is impossible to know which rule is being applied at a given iteration. TrueLife will also be a learning simulation where rules that produce better results will be applied more frequently. Another unique aspect of TrueLife is the motivation behind the rules. The original Life rules are Darwinian and selfish acting only on local inputs that lead to local outputs. TrueLife’s rules will be nonlocal and act globally across the entire grid. TrueLife’s rules were formalized by drawing on much broader areas of science such as ecology, psychology and quantum theory. We are currently in the process of finding a model system to which TrueLife would be best suited.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".