Deciphering Evolutionary Mechanisms Between Mutualistic and Pathogenic Symbioses.
Bibliographic record
Abstract
The continuum between mutualistic and pathogenic symbioses has been an underlying theme for understanding the evolution of infection and disease in a number of eukaryotic-microbe associations. The ability to monitor and then predict the spread of infectious diseases may depend upon our knowledge and capabilities of anticipating the behavior of virulent pathogens by studying related, benign symbioses. For instance, the ability of a symbiotic species to infect, colonize, and proliferate efficiently in a susceptible host will depend on a number of factors that influence both partners during the infection. Levels of virulence are not only affected by the genetic and phenotypic composite of the symbiont, but also the life history, mode(s) of transmission, and environmental factors that influence colonization, such as antibiotic treatment. Population dynamics of both host and symbiont, including densities, migration, as well as competition between symbionts will also affect infection rates of the pathogen as well as change the evolutionary dynamics between host and symbiont. It is therefore important to be able to compare the evolution of virulence between a wide range of mutualistic and pathogenic systems in order to determine when and where new infections might occur, and what conditions will render the pathogen ineffective. This perspective focuses on several symbiotic models that compare mutualistic associations to pathogenic forms and the questions posed regarding their evolution and radiation. A common theme among these systems is the prevailing concept of how heritable mutations can eventually lead to novel phenotypes and eventually new species.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".