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A CONSIDERATION OF PATTERNS OF VIRULENCE ARISING FROM HOST-PARASITE COEVOLUTION

2003· article· en· W2055621424 on OpenAlexafffund
Troy Day, James G. Burns

Bibliographic record

VenueEvolution · 2003
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEvolution and Genetic Dynamics
Canadian institutionsToronto ZooUniversity of TorontoQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCoevolutionBiologyHost (biology)VirulenceParasite hostingFecundityEvolutionary biologyZoologyEcologyGeneticsPopulationDemographyGene

Abstract

fetched live from OpenAlex

In this article we explore how host survival and fecundity are affected by host-parasite coevolution. We examine a situation in which hosts upon being infected can mount a defensive response to clear the infection, but in which there is a fecundity cost to such immunological up-regulation. We also suppose that the parasite exploits the host and thereby causes an elevated host mortality rate. We determine the coevolutionary stable strategies of the parasite's level of exploitation and the host's level of up-regulation, and illustrate the patterns of reduced host fitness (i.e., virulence) that these produce. We find that counterintuitive patterns of virulence are often expected to arise as a result of the interaction between coevolved host and parasite strategies. In particular, despite the fact that the parasite imposes only a mortality cost on the host, coevolution by the host results in a pattern whereby infected hosts always have the same probability of death from infection, but they vary in the extent to which their fecundity is reduced. This contrasts with previous results and arises from our inclusion of two important factors absent from previous theory: costs of immunological up-regulation and a more suitable measure of parasite-induced mortality.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.513
Threshold uncertainty score0.454

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.006
GPT teacher head0.235
Teacher spread0.229 · 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 designBench or experimental
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

Citations70
Published2003
Admission routes2
Has abstractyes

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