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Record W2808049856 · doi:10.1038/s41598-018-27515-w

Avian influenza surveillance in domestic waterfowl and environment of live bird markets in Bangladesh, 2007–2012

2018· article· en· W2808049856 on OpenAlexafffund
Salah Uddin Khan, Emily S. Gurley, Nancy Gerloff, Mohammed Ziaur Rahman, Natosha Simpson, Mustafizur Rahman, Najmul Haider, Sukanta Chowdhury, Amanda Balish, Rashid Zaman, Sharifa Nasreen, Bidhan Chandra Das, Eduardo Azziz‐Baumgartner, Katharine Sturm‐Ramirez, C. Todd Davis, Rubén O. Donis, Stephen P. Luby

Bibliographic record

VenueScientific Reports · 2018
Typearticle
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsUniversity of Guelph
FundersAgency for Toxic Substances and Disease RegistryCenters for Disease Control and PreventionStyrelsen för Internationellt UtvecklingssamarbeteGlobal Affairs CanadaMinistry of Health and Family WelfareDepartment for International DevelopmentInternational Centre for Diarrhoeal Disease Research, BangladeshUnited States Agency for International Development
KeywordsWaterfowlInfluenza A virus subtype H5N1GeographyHighly pathogenicFisheryEnvironmental healthZoologyBiologyVirologyEcologyMedicineVirus

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.062
Threshold uncertainty score0.123

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.323
Teacher spread0.291 · 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 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

Citations76
Published2018
Admission routes2
Has abstractyes

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