A Comparison of Novel Representations for Evolving Epidemic Networks
Bibliographic record
Abstract
Recent work in representation has developed small, evolvable structures called a complex string generator that generate infinite, aperiodic strings of characters. Such a string can be sectioned to provide an arbitrary list of parameters of indefinite length. Other work in evolving networks to model disease transmission has an issue common in many high-dimensional problems, evolution is less efficient when it must get a large number of parameter values correct. Specifying many parameters with a small evolvable object is a potential solution to this problem. In this study we compare three different implementations of representations, two of which employ complex string generators, to specify social contact graphs that plausibly explain the pattern of infection in a small epidemic. Representations that edit a starting network are found to have results that clump in network space while evolving the adjacency matrix provides increased diversity: none of the representations overlap in their results. The adjacency matrix based representation also generated outliers that outperform a baseline representation, probably because of its enhance diversity of solutions.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".