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Warming during different life stages has distinct impacts on host resistance ecology and evolution

2024· preprint· en· W4401864093 on OpenAlexaff
Jingdi Li, Cameron A. B. Smith, Jinlin Chen, Kayla C. King

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEvolution and Genetic Dynamics
Canadian institutionsUniversity of British Columbia
FundersNatural Environment Research CouncilPembroke College, University of Oxford
KeywordsHost (biology)BiologyEcologyPathogenResistance (ecology)Host resistanceExperimental evolutionVirulencePhenotypic plasticityGlobal warmingNatural selectionClimate changeGeneticsGeneSelection (genetic algorithm)

Abstract

fetched live from OpenAlex

Global climate change is causing extreme heating events and intensifying infectious disease outbreaks. We tested whether warming (at various host life stages) could shape the ecological and evolutionary trajectory of host resistance, by competing nematode host genotypes across 10 generations during infection by a natural bacterial pathogen. We found that persistent warming throughout host development and during infection strongly favoured genetic-based host resistance. Ambient temperatures or periodic warming within host lifetime resulted in the loss of genetic-based resistance, despite pathogen presence. Warming during host development caused plastic temperature-mediated protection which weakened selection for more costly resistance. The findings of an associated mechanistic model suggest that dilution of pathogen cells by resistant hosts might help protect susceptible individuals when warming does not occur during development. Host evolutionary trajectories were likely driven by the combination of fitness constraints on genetic-based resistance, host plasticity, condition-dependent pathogen virulence, and dilution effects.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.010
GPT teacher head0.249
Teacher spread0.239 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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