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Record W2135270248 · doi:10.1128/cmr.17.2.390-412.2004

Animal Pneumoviruses: Molecular Genetics and Pathogenesis

2004· review· en· W2135270248 on OpenAlexfundno aff
Andrew J. Easton, Joseph B. Domachowske, Helene F. Rosenberg

Bibliographic record

VenueClinical Microbiology Reviews · 2004
Typereview
Languageen
FieldMedicine
TopicRespiratory viral infections research
Canadian institutionsnot available
FundersUniversity of Saskatchewan
KeywordsHuman metapneumovirusVirologyBiologyParamyxoviridaeMetapneumovirusVirusRNA virusPneumovirusPneumovirinaeMononegaviralesInnate immune systemPneumoniaImmunologyViral diseaseRNARespiratory tract infectionsGeneGeneticsImmune systemRespiratory systemMedicine

Abstract

fetched live from OpenAlex

Pneumoviruses are single-stranded, negative-sense, nonsegmented RNA viruses of the family Paramyxoviridae, subfamily Pneumovirinae, and include pathogens that infect humans (respiratory syncytial virus and human metapneumovirus), domestic mammals (bovine, ovine, and caprine respiratory syncytial viruses), rodents (pneumonia virus of mice), and birds (avian metapneumovirus). Among the topics considered in this review are recent studies focused on the roles of the individual virus-encoded components in promoting virus replication as well as in altering and evading innate antiviral host defenses. Advances in the molecular technology of pneumoviruses and the emergence of recombinant pneumoviruses that are leading to improved virus-based vaccine formulations are also discussed. Since pneumovirus infection in natural hosts is associated with a profound inflammatory response that persists despite adequate antiviral therapy, we also review the recent experimental treatment strategies that have focused on combined antiviral, anti-inflammatory, and immunomodulatory approaches.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.003

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 head0.255
GPT teacher head0.517
Teacher spread0.263 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations206
Published2004
Admission routes1
Has abstractyes

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