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Record W2897032734 · doi:10.1016/s2214-109x(18)30418-2

The cost and challenge of vaccine development for emerging and emergent infectious diseases

2018· letter· en· W2897032734 on OpenAlexaboutno aff
Joel N. Maslow

Bibliographic record

VenueThe Lancet Global Health · 2018
Typeletter
Languageen
FieldMedicine
TopicViral Infections and Outbreaks Research
Canadian institutionsnot available
Fundersnot available
KeywordsZika virusMiddle East respiratory syndromeOutbreakMedicineDengue feverMiddle East respiratory syndrome coronavirusVirologyInfectious disease (medical specialty)DiseaseVirusCoronavirus disease 2019 (COVID-19)Internal medicine

Abstract

fetched live from OpenAlex

Over the past two decades, new infectious threats have emerged almost yearly.1 Outbreaks of Ebola virus disease, severe acute respiratory syndrome, Middle East respiratory syndrome, and Zika virus disease were interspersed between epidemics of H5N1 and H7N9 avian, swine, and H1N1 influenza. While most developed countries focused on these, disease due to Lassa, Nipah, and Crimean-Congo haemorrhagic fever viruses continued unabated. Other emergent pathogens such as severe fever and thrombocytopenia syndrome virus, and Powassan virus remain restricted in scope.

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)0.021
metaresearch head score (Gemma)0.040
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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.034
Threshold uncertainty score0.113

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.040
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0040.009
Open science0.0020.003
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0340.010

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 head0.053
GPT teacher head0.402
Teacher spread0.349 · 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
GenreCommentary

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

Citations19
Published2018
Admission routes1
Has abstractyes

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