Transience in the simulation of ring species
Bibliographic record
Abstract
Biological ring species theoretically develop when an ancestral population expands around a geographic barrier and differentiates until terminal populations come back into contact. Adjacent populations are fertile; fertility declines with distance, and the terminal populations are not fertile. This study uses evolutionary algorithms to attempt to create artificial ring species using grid robots performing the Tartarus task with ISAc lists and string genes solving the Self Avoiding Walk (SAW) problem. Three experiments are done with the Tartarus robots. Fertility is shown to decrease with distance, but not to the extent that ring species are formed. Two experiments are done with SAW. These experiments produce sub-populations which satisfy all the criteria for biological ring species at the point in time when the ring closes. As evolution continues, the relationship between fertility and distance continues, but the terminal populations do not remain infertile. In addition, on both problems, record scores are achieved, suggesting that this model of evolution is a good optimizer for multi-optima problems like Tartarus and SAW which have many deceptive suboptima.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".