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Record W345517797 · doi:10.1007/978-3-319-13755-1_6

Ranavirus Host Immunity and Immune Evasion

2015· book-chapter· en· W345517797 on OpenAlexfundno aff
Leon Grayfer, Eva‐Stina Edholm, Francisco De Jesús Andino, V. Gregory Chinchar, Jacques Robert

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldMedicine
TopicViral Infections and Vectors
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaLife Sciences Research FoundationWashington State UniversityHoward Hughes Medical InstituteVanderbilt UniversityNational Institutes of HealthNational Science Foundation
KeywordsRanavirusBiologyImmune systemHost (biology)Evasion (ethics)ImmunityVertebrateEvolutionary biologyImmunologyEcologyVirusGeneGenetics

Abstract

fetched live from OpenAlex

Ranaviruses (RV, Iridoviridae) are now known to infect fish, amphibians, and reptiles, raising considerable ecological and commercial concerns due to the escalating infection prevalence and the resulting die-offs of wild and aquacultural species. Notably, ranaviruses exhibit uncanny capacities to cross host species barriers of their poikilothermic hosts, likely owing to their potent immune evasion mechanisms. In turn, the species infected by these pathogens possess immune systems that are less well understood than those of mammals, and are often comprised of unique immune genes or multiple copy orthologs of the single hallmark mammalian immune factors. Thus, garnering greater insight into ranavirus infection strategies is largely contingent on gaining further insights into host immune barriers faced by these emerging infectious agents. Accordingly, here we coalesce the current state of understanding of the distinct facets of lower vertebrate immune responses to ranaviral infections and underline some of the evasion strategies by which these pathogens circumvent these host defenses.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.834
Threshold uncertainty score0.998

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)0.0030.000

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 head0.069
GPT teacher head0.307
Teacher spread0.238 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations39
Published2015
Admission routes1
Has abstractyes

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