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Record W2528003671 · doi:10.1021/acsinfecdis.6b00161

Zika Virus: Emergence, Phylogenetics, Challenges, and Opportunities

2016· review· en· W2528003671 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueACS Infectious Diseases · 2016
Typereview
Languageen
FieldMedicine
TopicMosquito-borne diseases and control
Canadian institutionsMcGill University
FundersFonds de recherche du Québec – Nature et technologiesFaculty of Medicine, McGill UniversityNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsZika virusMicrocephalyPandemicVirologyBiologyVirusMedicineImmunologyDiseaseCoronavirus disease 2019 (COVID-19)Infectious disease (medical specialty)PathologyGenetics

Abstract

fetched live from OpenAlex

Zika virus (ZIKV) is an emerging arthropod-borne pathogen that has recently gained notoriety due to its rapid and ongoing geographic expansion and its novel association with neurological complications. Reports of ZIKV-associated Guillain-Barré syndrome as well as fetal microcephaly place emphasis on the need to develop preventative measures and therapeutics to combat ZIKV infection. Thus, it is imperative that models to study ZIKV replication and pathogenesis and the immune response are developed in conjunction with integrated vector control strategies to mount an efficient response to the pandemic. This paper summarizes the current state of knowledge on ZIKV, including the clinical features, phylogenetic analyses, pathogenesis, and the immune response to infection. Potential challenges in developing diagnostic tools, treatment, and prevention strategies are also discussed.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.980
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
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 head0.074
GPT teacher head0.328
Teacher spread0.254 · 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