Bibliographic record
Abstract
Biological ring species are a population surrounding a geographic obstruction such as a large lake or a mountain range. Adjacent sub-populations are mutually fertile, but fertility drops with distance. This study attempts to create examples of artificial ring species using evolutionary algorithms. ISAc lists, a representation with self-organized and potentially complex genetics, are used to evolve controllers for the Tartarus task. The breeding population of Tartarus controllers are arranged in a ring-shaped configuration with strictly local gene flow. Fertility is defined to be the probability that a child will have fitness at least that of its least fit parent. Fertility is found to drop steadily and significantly with distance around the ring in each of twelve replicates of the experiment. Comparison of fertility at various distances within a ring-shaped population is compared with sampled intra-population fertility. Some populations are found to have significantly higher than background fertility with other populations. This phenomena suggests the presence of aggressive genetics or dominant phenotype in which a creature has an enhanced probability of simply cloning its own phenotype during crossover. In addition to creating examples of artificial ring species this study also achieved a very high level of fitness with the Tartarus task. A comparison is made with another study that uses hybridization to achieve record breaking Tartarus fitness.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".