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Record W2808339545 · doi:10.1016/s2214-109x(18)30302-4

New global strategic plan to eliminate dog-mediated rabies by 2030

2018· article· en· W2808339545 on OpenAlexaboutno aff
Matthew D. Stone, Maria Helena Semedo, Louis H. Nel

Bibliographic record

VenueThe Lancet Global Health · 2018
Typearticle
Languageen
FieldImmunology and Microbiology
TopicRabies epidemiology and control
Canadian institutionsnot available
FundersWorld Health Organization
KeywordsRabiesOne HealthLatin AmericansMedicineDog biteGeographyGlobal healthSocioeconomicsEnvironmental healthPolitical sciencePublic healthVirologyLawPathologySociology

Abstract

fetched live from OpenAlex

Rabies is one of the oldest and most terrifying diseases known to man. Written and pictorial records of rabies date back more than 4000 years, and today it is endemic in more than 150 countries around the world.1 Even though the disease can be prevented, it kills an estimated 59 000 people each year,2 mostly in the world's poorest and most vulnerable communities. About 40% of the victims are children younger than 15 years living in Asia and Africa. A staggering 99% of human cases are acquired via the bite of an infected dog, rather than through exposures to the many and varied wild animals that act as viral reservoirs on different continents.

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)0.005
metaresearch head score (Gemma)0.006
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: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.052
Threshold uncertainty score0.176

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0040.004
Open science0.0040.006
Research integrity0.0100.006
Insufficient payload (model declined to judge)0.0520.037

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 head0.038
GPT teacher head0.345
Teacher spread0.307 · 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
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

Citations151
Published2018
Admission routes1
Has abstractyes

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