KIN-BASED RECOGNITION AND SOCIAL AGGREGATION IN A CILIATE
Bibliographic record
Abstract
Aggregative groups entail costs that must be overcome for the evolution of complex social interactions. Understanding the mechanisms that allow aggregations to form and restrict costs of cheating can provide a resolution to the instability of social evolution. Aggregation in Tetrahymena thermophila is associated with costs of reduced growth and benefits of improved survival through "growth factor" exchange. We investigated what mechanisms contribute to stable cooperative aggregation in the face of potential exploitation by less-cooperative lines using experimental microcosms. We found that kin recognition modulates aggregative behavior to exclude cheaters from social interactions. Long-distance kin recognition across patches modulates social structure by allowing recruitment of kin in aggregative lines and repulsion in asocial lines. Although previous studies have shown a clear benefit to social aggregation at low population densities, we found that social aggregation has very different effects at higher densities. Lower growth rates are a cost of aggregation, but also present potential benefits when restricted to kin aggregations: slow growth and crowd tolerance allow aggregations to form and permit longer persistence on ephemeral resources. Thus in highly dynamic metapopulations, kin recognition plays an important role in the formation and stability of social groups that increase persistence through cooperative consumptive restraint.
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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 | 0.001 | 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) | 0.000 | 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".