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Record W2996248465 · doi:10.1101/2019.12.18.881383

Prophylactic host behaviour discourages pathogen exploitation

2019· preprint· en· W2996248465 on OpenAlexafffund
Evan Mitchell, Geoff Wild

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2019
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEvolution and Genetic Dynamics
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPathogenHost (biology)Transmission (telecommunications)VirulenceBiologyPopulationExploitImmunologyEcologyMedicineEnvironmental healthComputer scienceGeneticsComputer securityGene

Abstract

fetched live from OpenAlex

Summary Much work has considered the evolution of pathogens, but little is known about how they respond to changes in host behaviour. We build a model where hosts are able to choose to engage in prophylactic measures that reduce the likelihood of disease transmission. This choice is mediated by costs and benefits associated with prophylaxis, but the fraction of hosts engaged in prophylaxis is also affected by population dynamics. We identify a critical cost threshold above which hosts do not engage in prophylaxis. Below the threshold, prophylactic host behaviour does occur and pathogen virulence, measured by the extent to which it exploits its host, is reduced by the action of selection relative to the level that would otherwise be predicted in the absence of prophylaxis. Our work emphasizes the significance of the dual nature of the trade-off faced by the pathogen between balancing transmission and recovery, and creating new infections in hosts engaging or not engaging in prophylaxis.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.009
GPT teacher head0.225
Teacher spread0.216 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2019
Admission routes2
Has abstractyes

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