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Record W4394075574 · doi:10.6084/m9.figshare.15081831

Emerging highly pathogenic avian influenza (H5N8) virus in migratory birds in Central China, 2020

2021· dataset· en· W4394075574 on OpenAlexaff
Jiasong Xiong, Hao Zhou, Lifang Fan, Gongliang Zhu, Yong Li, Guang Chen, Jundong Li, Hesong Zheng, Wei Feng, Jing Chen, Guoxiang Yang, Quanjiao Chen

Bibliographic record

VenueFigshare · 2021
Typedataset
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsAlberta Health Services
Fundersnot available
KeywordsHighly pathogenicInfluenza A virus subtype H5N1VirologyChinaAvian influenza virusBiologyVirusH5N1 genetic structureGeographyCoronavirus disease 2019 (COVID-19)MedicineInfectious disease (medical specialty)Disease

Abstract

fetched live from OpenAlex

Eleven highly pathogenic avian influenza H5N8 viruses (clade 2.3.4.4b) were detected in migratory birds in Central China between November and December 2020, which were highly homologous to strains isolated in Europe from October to December 2020. Phylogenetic analysis indicated that the strains in the study possibly spread from Siberia by migratory birds. In this study, we found H5N8 virus infection in migratory birds could cause severe pathological damage and high viral load in multiple organs.

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)0.001
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.047
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.005
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0140.004

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 head0.055
GPT teacher head0.354
Teacher spread0.298 · 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
GenreDataset

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

Citations0
Published2021
Admission routes1
Has abstractyes

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