Defector clustering is linked to cooperation in a pathogenic bacterium
Bibliographic record
Abstract
Spatial clustering is thought to favour the evolution of cooperation because it puts cooperators in a position to help each other. However, clustering also increases competition. The fate of cooperation may depend on how much cooperators cluster relative to defectors, but these clustering differences have not been the focus of previous models and experiments. By competing siderophore-producing cooperator and defector strains of the opportunistic pathogenPseudomonas aeruginosain experimental microhabitats, we found that at the spatial scale of individual interactions, cooperator clustering lowers cooperation, but defector clustering favours cooperation. A theoretical model and individual-based simulations show these counterintuitive effects can arise when competition and cooperation occur at a single resource-determined scale, with population dynamics crucially allowing cooperators and defectors to cluster differently. The results suggest that cooperation relies on the regulation of sufficient defector clustering relative to cooperator clustering, which may be important in bacteria, social amoeba and cancer inhibition.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 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".