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Record W3083646714 · doi:10.1186/s12916-020-01755-y

Preparing for a pandemic: highlighting themes for research funding and practice—perspectives from the Global Research Collaboration for Infectious Disease Preparedness (GloPID-R)

2020· letter· en· W3083646714 on OpenAlexafffund
Alice Norton, Louise Sigfrid, Adeniyi Kolade Aderoba, Naima Nasir, Peter Bannister, Shelui Collinson, James Lee, Geneviève Boily-Larouche, Josephine P Golding, Evelyn Depoortere, Gail Carson, Barbara Kerstiëns, Yazdan Yazdanpanah

Bibliographic record

VenueBMC Medicine · 2020
Typeletter
Languageen
FieldMathematics
TopicCOVID-19 epidemiological studies
Canadian institutionsCanadian Institutes of Health ResearchInstitute of Infection and ImmunityGovernment of Canada
FundersEuropean and Developing Countries Clinical Trials PartnershipCanadian Institutes of Health ResearchNational Institutes of HealthFundação Oswaldo CruzDalhousie UniversityUniversity of OxfordNational Institute for Health and Care ResearchMedical Research CouncilLondon School of Hygiene and Tropical MedicineEuropean Bioinformatics InstituteCoalition for Epidemic Preparedness InnovationsWellcome TrustEuropean CommissionBill and Melinda Gates FoundationBritish Columbia Centre for Disease ControlJapan Agency for Medical Research and Development
KeywordsMedicinePreparednessPandemicCoronavirus disease 2019 (COVID-19)Infectious disease (medical specialty)2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)DiseaseFamily medicineVirologyOutbreakPathologyManagement

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.013
metaresearch head score (Gemma)0.543
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.530
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.543
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.644
GPT teacher head0.599
Teacher spread0.045 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations17
Published2020
Admission routes2
Has abstractno

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