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Record W2573504792 · doi:10.1177/153567601301800102

Biosafety Risk Assessment and Management of Laboratory-Derived Influenza A (H5N1) Viruses Transmissible in Ferrets

2013· article· en· W2573504792 on OpenAlexfundno aff
Aline Baldo, Amaya Leunda, Chuong Daï Do Thi, Didier Breyer, Katia Pauwels, Sarah Welby, Bernadette Van Vaerenbergh, Philippe Herman

Bibliographic record

VenueApplied Biosafety · 2013
Typearticle
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsnot available
FundersPublic Health Agency of CanadaUniversity of Wisconsin-Madison
KeywordsInfluenza A virus subtype H5N1BiosafetyHighly pathogenicVirologyPandemicTransmission (telecommunications)Context (archaeology)BiosecurityBiologyInfluenza A virusVirusMedicineCoronavirus disease 2019 (COVID-19)BiotechnologyInfectious disease (medical specialty)DiseaseEngineering

Abstract

fetched live from OpenAlex

Highly pathogenic avian influenza (HPAI) A (H5N1) viruses occasionally infect humans, but currently do not transmit efficiently among them. However, the risk for human pandemic influenza is a major concern should these viruses acquire the capacity for human-to-human transmission and retain their current virulence. Recently, two research teams have succeeded in modifying HPAI A (H5N1) viruses in such a way that they could be efficiently transmitted by respiratory route between ferrets, the experimental model for studying influenza virus transmission. In this article, the authors discuss the risk assessment of these mutant HPAI A (H5N1) viruses in the context of the European Union regulatory framework and recommend that laboratory-derived HPAI A (H5N1) viruses transmissible in ferrets should be handled in biosafety level 3 (BSL-3) facilities with some additional biosafety measures.

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)0.004
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.051
GPT teacher head0.368
Teacher spread0.317 · 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 designBench or experimental
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

Citations3
Published2013
Admission routes1
Has abstractyes

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