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Record W7014158356

A one health priority research agenda for antimicrobial resistance

2023· other· en· W7014158356 on OpenAlexfundno aff

Bibliographic record

VenueNERC Open Research Archive (Natural Environment Research Council) · 2023
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
FundersCorporación colombiana de investigación agropecuariaInternational Livestock Research InstituteUniversiteit AntwerpenFreie Universität BerlinNational Research FoundationUniversity of PretoriaUniversitat de BarcelonaDanmarks Tekniske UniversitetEuropean Food Safety AuthorityChulalongkorn UniversityPublic Health AgencyIndian Council of Agricultural ResearchKing Abdullah University of Science and TechnologyUniversity of OxfordAlexandria UniversityUniversity of WashingtonImperial College LondonMbarara University of Science and TechnologyUniversidade de São PauloWorld Health OrganizationGeorge Washington UniversityIndian Agricultural Research InstituteNorth-West UniversityPublic Health Agency of Canada
KeywordsOne HealthLeverage (statistics)AgricultureAnimal healthStakeholderPublic healthGlobal healthAntibiotic resistance
DOInot available

Abstract

fetched live from OpenAlex

The One Health Priority Research Agenda on Antimicrobial Resistance (AMR) sets out for the first time the priorities for which the World Health Organization, the Food and Agriculture Organization of the United Nations, the United Nations Environment Programme, and the World Organisation for Animal Health – as leaders in the multilateral system on human, animal, plant, and environmental health – will advocate to promote research and investment in the response to AMR. 
\nThe Research Agenda results from extensive stakeholder and expert engagement and was developed using a sound scientific methodology. The process identified major gaps in knowledge and evidence that require urgent scientific attention and resources. The document demonstrates how, by working together, we can effectively leverage our organizations’ respective resources and strengths in the multilateral system.

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)0.228
metaresearch head score (Gemma)0.036
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Scholarly communication, Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Open science, Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.192
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.2280.036
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0070.006
Science and technology studies0.0070.012
Scholarly communication0.0030.001
Open science0.0160.023
Research integrity0.0010.023
Insufficient payload (model declined to judge)0.0040.040

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 head0.332
GPT teacher head0.444
Teacher spread0.112 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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
Published2023
Admission routes1
Has abstractyes

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Same venueNERC Open Research Archive (Natural Environment Research Council)French-language works237,207