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Record W2021931705 · doi:10.1098/rspb.2001.1795

Interactions between sources of mortality and the evolution of parasite virulence

2001· article· en· W2021931705 on OpenAlexafffund
Paul Williams, Troy Day

Bibliographic record

VenueProceedings of the Royal Society B Biological Sciences · 2001
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEvolution and Genetic Dynamics
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsVirulenceHost (biology)Mortality rateBiologyPathogenParasite hostingEvolutionary biologyEcologyMicrobiologyDemographyGeneticsGeneComputer science

Abstract

fetched live from OpenAlex

A well-known result from the theory of the evolution of virulence is the prediction that the virulence of a pathogen (i.e. the rate of parasite-induced host mortality) always evolves to higher levels when host background mortality rates increase. This prediction, however, is derived from models that assume that host mortality sources combine additively to determine the overall host mortality rate. In this paper, we suggest that such additivity is probably rare for many host-pathogen systems, and explore how the predictions for the evolution of virulence are altered when interactions between host mortality sources are incorporated into the theory. Our results indicate that if mortality-source interactions are sufficiently strong then the evolutionarily stable level of virulence can actually decrease as the background mortality rate increases. Consequently, a detailed mechanistic description of how parasites and other mortality sources combine to cause host mortality is required before reliable predictions about virulence evolution can be made. Moreover, mortality-source interactions make empirical comparisons of the virulence of different parasites a much more subtle issue.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.868

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.025
GPT teacher head0.289
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations81
Published2001
Admission routes2
Has abstractyes

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