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Record W4282918066 · doi:10.1038/s41587-022-01357-4

The GA4GH Phenopacket schema defines a computable representation of clinical data

2022· letter· en· W4282918066 on OpenAlexafffund
Julius O.B. Jacobsen, Michael Baudis, Gareth Baynam, J. Beckmann, Sergi Beltrán, Orion J. Buske, Tiffany J. Callahan, Christopher G. Chute, Mélanie Courtot, Daniel Daniš, Olivier Elemento, Andrea Essenwanger, Robert R. Freimuth, Michael Gargano, Tudor Groza, Ada Hamosh, Nomi L. Harris, Rajaram Kaliyaperumal, K. C. Kent Lloyd, Aly Khalifa, Peter Krawitz, Sebastian Köhler, Bryan Laraway, Heikki Lehväslaiho, Leslie Matalonga, Julie A. McMurry, Alejandro Metke‐Jimenez, Chris Mungall, Mónica Muñoz-Torres, Soichi Ogishima, Anastasios Papakonstantinou, Davide Piscia, Nikolas Pontikos, Núria Queralt-Rosiñach, Marco Roos, Julian Saß, Paul N. Schofield, Dominik Seelow, Anastasios Siapos, Damian Smedley, Lindsay Smith, Robin Steinhaus, Jagadish Chandrabose Sundaramurthi, Emilia M. Swietlik, Sylvia Thun, Nicole Vasilevsky, Alex H. Wagner, Jeremy L. Warner, Claus Weiland, Myles Axton, Lawrence Babb, Cornelius F. Boerkoel, Bimal P. Chaudhari, Hui‐Lin Chin, Michel Dumontier, Nour Gazzaz, Harry Hochheiser, Veronica A. Kinsler, Hanns Lochmüller, Alexander Mankovich, Gary Saunders, Panagiotis I. Sergouniotis, Rachel Thompson, Andreas Zankl, Melissa Haendel, Peter N. Robinson

Bibliographic record

VenueNature Biotechnology · 2022
Typeletter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Rare Diseases
Canadian institutionsOttawa HospitalChildren's Hospital of Eastern OntarioUniversity of OttawaUniversity of British ColumbiaOntario GenomicsB.C. Women's Hospital & Health CentreOntario Institute for Cancer Research
FundersLawrence Berkeley National LaboratoryUniversity of California Davis School of MedicineUniversity College LondonUniversité de LausanneNational Cancer InstituteNational Human Genome Research InstituteLeibniz-GemeinschaftCanadian Institutes of Health ResearchAnschutz Medical Campus, University of ColoradoNational Institutes of HealthGenome CanadaEuropean Molecular Biology LaboratoryAngela Wright Bennett FoundationNational Health and Medical Research CouncilVanderbilt UniversityMoorfields Eye CharityMoorfields Eye Hospital NHS Foundation TrustCommonwealth Scientific and Industrial Research OrganisationFaculty of Health and Medical Sciences, University of Western AustraliaEuropean Bioinformatics InstituteFreie Universität BerlinDepartment of Artificial Intelligence, Korea UniversityUniversität ZürichU.S. Department of Health and Human ServicesEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentHumboldt-Universität zu BerlinAustralian e-Health Research CentreAlan Turing InstituteNational Center for Advancing Translational SciencesMedical Research CouncilLeids Universitair Medisch CentrumUniversiteit LeidenUniversity of ConnecticutWeill Cornell Medical CollegeQueen Mary University of LondonNational Institute for Health and Care ResearchStan Perron Charitable FoundationUniversitat Pompeu FabraWellcome TrustUniversitat de BarcelonaOhio State UniversityBarcelona Institute of Science and TechnologyChina Scholarship CouncilJapan Agency for Medical Research and DevelopmentJohns Hopkins UniversityMcCusker Charitable FoundationBerlin Institute of HealthNationwide Children's HospitalU.S. Department of Energy
KeywordsSchema (genetic algorithms)Representation (politics)Computer scienceInformation retrievalPolitical science

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 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)0.006
metaresearch head score (Gemma)0.036
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: Other · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.036
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0040.003
Open science0.0020.003
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0170.006

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 head0.039
GPT teacher head0.355
Teacher spread0.315 · 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
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

Citations111
Published2022
Admission routes2
Has abstractno

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