Virulence evolution in a host-parasite system in the absence of viral evolution.
Bibliographic record
Abstract
QUESTION: /sigma virus (DMelSV) system? ORGANISMS: (host) and DMelSV (parasite). EMPIRICAL METHODS: Artificial selection on whole-carcass viral titre of infected flies, including two selection regimes (maternal and biparental transmission) and three treatments within each regime (increased titre, decreased titre, and control). The maternal transmission selection regime lasted for six generations, while the biparental transmission selection regime lasted for twelve generations. We further quantified virulence by estimating the fecundity, viability, and development time of infected flies. Finally, we sequenced virus strains at the end of selection. PREDICTIONS AND CONCLUSIONS: Titre is defined here as the number of viral genomes inside a single fly, while virulence is defined as harm to host. We predicted that titre would respond to both increased and decreased selection, that virulence would evolve as a positively correlated response, and that sequence evolution in the viruses would be responsible for these changes. Titre did respond to selection in the biparental regime, although both high and control lines both demonstrated increased titre, while the titre of the low lines did not change. One component of virulence, development time, was positively correlated with titre in the biparental transmission lines (maternal transmission lines were not scored for virulence). However, we detected few (and in some cases, no) genomic changes in the virus, making viral evolution unlikely to be responsible for the response to selection and the association between development time and titre.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.001 | 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".