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Record W4409582160 · doi:10.1002/9783527845088.ch00

Introduction

2025· other· en· W4409582160 on OpenAlexaff

Bibliographic record

Venuenot available
Typeother
Languageen
FieldImmunology and Microbiology
TopicHIV Research and Treatment
Canadian institutionsArbutus Biopharma (Canada)
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

Humanity has long been plagued by various viral pathogens, but only in recent history has the discovery and development of specific and efficacious antiviral drugs provided important countermeasures. Historically, antiviral drug discovery has mainly targeted major chronic viral infections, including human herpesviruses, human immunodeficiency virus, hepatitis B virus and hepatitis C virus. Although the lack of a cure for these viruses, except for hepatitis C virus, will likely continue to drive most antiviral research and development efforts, emerging and reemerging acute viral infections, such as influenza viruses, SARS-CoV-2, and Mpox virus, have been the targets of recent success and increasing attention. This chapter provides an overview of those drugs that have been developed to address significant viral diseases over the last 60 years.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.598
Threshold uncertainty score0.853

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.4020.267

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 head0.007
GPT teacher head0.253
Teacher spread0.247 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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