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Record W4391168695 · doi:10.56367/oag-041-11224

MPOX: Research priorities for threat reduction

2024· article· en· W4391168695 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.

Bibliographic record

VenueOpen Access Government · 2024
Typearticle
Languageen
FieldImmunology and Microbiology
TopicPoxvirus research and outbreaks
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsReduction (mathematics)Computer sciencePsychologyMathematics

Abstract

fetched live from OpenAlex

MPOX: Research priorities for threat reduction Concerted efforts are needed to close knowledge gaps around mpox to improve preparedness and response efforts for this neglected disease. Mpox, formerly known as monkeypox, is a previously neglected re-emerging zoonotic disease caused by monkeypox virus (MPV), genus Orthopoxvirus, family Poxviridae. This virus can cause severe illness in infected patients and is endemic in numerous countries across Central and West Africa, including the Democratic Republic of Congo (DRC), Nigeria, Cameroon, Sierra Leone, Ghana, Liberia, and others. However, in 2022, a global mpox outbreak led to the declaration of a public health emergency of international concern by the World Health Organization, with more than 90,000 confirmed cases reported from non-endemic global regions. While swift responses to this outbreak helped reduce case trends across highly impacted regions by Fall 2022, including the distribution of vaccine and therapeutics to at-risk communities, as well as increased public awareness, ongoing outbreaks in endemic regions continue to have deleterious effects on public health.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication, Insufficient 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: Empirical · Consensus signal: none
Teacher disagreement score0.488
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.191
GPT teacher head0.493
Teacher spread0.302 · 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