Quorum Sensing and Bacterial Social Interactions in Biofilms: Bacterial Cooperation and Competition
Bibliographic record
Abstract
The natural life of bacteria involves complex social interactions that can be either competitive or cooperative. Competition between species is well recognized as a major ecological force to drive bacterial evolution. However, it was not until the last two decades that bacterial cooperative activities were recognized to play important roles in bacterial ecology, evolution, and infectious diseases. In some cases, cooperative activities may be essential for bacterial survival, competition, and success in nature. Many bacteria are known to regulate cooperative activities through a quorum-sensing mechanism, which enables the cells to coordinate population-wide adaptive responses. The ability of bacteria to communicate and behave as a group like a multicellular organism has provided great benefits to bacteria. Importantly, many quorum-sensing-mediated activities have been involved in bacterial virulence and pathogenesis. However, evolutionary theory predicts that cooperative cells often benefit the group at their own cost, which can be exploited by noncooperators in the populations. This conflict has raised many questions regarding bacterial cooperation and promoted great interest in studying ecological and evolutionary aspects of bacterial social interactions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".