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Record W4307031703 · doi:10.1080/22221751.2022.2136537

Multiple genotypes of Crimean-Congo hemorrhagic fever virus detected in ticks during a one health survey in Agnam, Northeastern Senegal

2022· letter· en· W4307031703 on OpenAlexfundno aff
Moufid Mhamadi, Aminata Badji, Idrissa Dieng, Alioune Gaye, El Hadji Ndiaye, Mignane Ndiaye, Moundhir Mhamadi, Cheikh Talibouya Touré, Aliou Barry, Oumar Ndiaye, Babacar Faye, Fatimata Amadou Ba, Boly Diop, Mamadou Ndiaye, Samba Niang Sagne, Gamou Fall, Cheikh Loucoubar, Hugues Fausther Bovendo, Amadou Alpha Sall, Gary Kobinger, Ousmane Faye, Mawlouth Diallo, Oumar Faye

Bibliographic record

VenueEmerging Microbes & Infections · 2022
Typeletter
Languageen
FieldMedicine
TopicViral Infections and Vectors
Canadian institutionsnot available
FundersInternational Development Research Centre
KeywordsCrimean–Congo hemorrhagic feverVirologyGenotypeBiologyTickVeterinary medicineMedicineGeneGenetics

Abstract

fetched live from OpenAlex

A Crimean-Congo Hemorrhagic Fever Virus (CCHFV) survey in Agnam (North Senegal) permits the detection of three isolates in ticks. These isolates belong genetically to multiple genotypes (I, II, III) and clustered with strains from Uganda, Sudan, Mauritania, and Senegal. The role of ticks in CCHF emergence and widespread is highlighted.

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 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 categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.003
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 head0.032
GPT teacher head0.277
Teacher spread0.246 · 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 teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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

Citations8
Published2022
Admission routes1
Has abstractyes

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