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Record W3004764316 · doi:10.1016/j.sciaf.2020.e00309

Outsmarting Ebola through stronger national health systems

2020· article· en· W3004764316 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

VenueScientific African · 2020
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Outbreaks Research
Canadian institutionsThe Scarborough HospitalPublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsSierra leoneOutbreakEbola virusEbola Hemorrhagic FeverGeographyDemocracySri lankaSocioeconomicsEconomic growthPolitical scienceVirologyMedicineEnvironmental planningLawPoliticsSociologyEconomics

Abstract

fetched live from OpenAlex

The current outbreak occurring in the Congo highlights the continuous challenge that the continent of Africa faces with Ebola outbreaks. Since the first recorded Ebola outbreak occurred simultaneously in Democratic Republic of Congo (DRC) and in Sudan in 1976, there have been 27 recorded outbreaks in Africa by country with the most severe occurring in Guinea, Liberia and Sierra Leone in 2014. In this Letter to the Editor, we argue that the best way to outsmart such a pathogen in Africa is through investments in stronger national health systems.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.498
Threshold uncertainty score0.471

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.132
GPT teacher head0.384
Teacher spread0.252 · 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