AGING IN HOSTILE ENVIRONMENT MODELED BY CELLULAR AUTOMATA WITH GENETIC DYNAMICS
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.
Bibliographic record
Abstract
We model the evolution of a population on a 2D cellular automata (CA) lattice. Every individual holds a binary "genetic code". The code length and the number of "1"s in the chain correspond to the maximal and actual life-time of individual, respectively. The "genetic code" code is divided onto three life-episodes: "youth", "maturity" and "old age". Only "mature" individuals can procreate. We investigate the duration of the life-episodes and their role in protecting the population from extinction in hostile environments. We observe that in the stable environment, which does not influence the life-time of individuals, the "youth" and the "maturity" periods extend extremely long during evolution, while the "old age" remains short. The situation is different for hostile plaque-like conditions. Under these circumstances, the "youth" period vanishes, while the longer "old age" period stabilizes the population growth, increases its average age and thereby increases its chance of survival. We can conclude that the idle life-episodes set up the control mechanisms, which allow for self-adaptation of the population to varying environmental conditions.
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 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 it