Bibliographic record
Abstract
The relationship between complexity and various factors is explored through the simulation of the three neighbor ways of the game of life. It mainly discusses the state evolution process of cell populations under various evolutionary laws, various environmental scales and various initial states. Based on the discovery of a novel, long-lived and simple cell with an initial state, the periodic and stable cell morphology in Game of Life is introduced, thus reflecting the related complexity factors and changes. By simulating various environmental boundaries and comparing the steady-state graphs, it is concluded that a closed system will cause certain limitations in the final outcome. The limited environment will prevent the cell from expanding outward, but it can also create more periodic patterns. A limited environment is simultaneously an important factor in simplifying the system. In addition to the environment, the edge of chaos is also an important factor in the complexity of the system. An appropriate evolution rule can help the entire system find a balance in the chaos and present stable and interesting patterns. In addition, the correct neighbor method has a positive effect on the change of the cell. Finally, an infinite loop mode is set up to illustrate once again the wonder and complexity of Game of Life.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 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".