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Record W2170282111 · doi:10.1093/nar/gkt1026

The Human Phenotype Ontology project: linking molecular biology and disease through phenotype data

2013· article· en· W2170282111 on OpenAlexaff
Sebastian Köhler, Sandra C. Doelken, Chris Mungall, Sebastian Bauer, Helen V. Firth, Isabelle Bailleul‐Forestier, Graeme C. Black, Danielle L. Brown, Michael Brudno, Jennifer Campbell, David Fitzpatrick, Janan T. Eppig, Andrew P. Jackson, Kathleen Freson, Marta Gîrdea, Ingo Helbig, Jane A. Hurst, Johanna Jähn, Laird G. Jackson, Anne M. Kelly, David H. Ledbetter, Sahar Mansour, Christa Lese Martin, Celia Moss, Andrew Mumford, Willem H. Ouwehand, Soo-Mi Park, Erin Rooney Riggs, Richard H. Scott, Sanjay M. Sisodiya, Steven Van Vooren, Ronald J. Wapner, Andrew O.M. Wilkie, Caroline F. Wright, Anneke T. Vulto‐van Silfhout, Nicole de Leeuw, Bert B.A. de Vries, Nicole L. Washingthon, Cynthia L. Smith, Monte Westerfield, Paul N. Schofield, Barbara J. Ruef, Georgios V. Gkoutos, Melissa Haendel, Damian Smedley, Suzanna Lewis, Peter N. Robinson

Bibliographic record

VenueNucleic Acids Research · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBiomedical Text Mining and Ontologies
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
FundersBasic Energy SciencesNational Institute of Mental HealthNational Institutes of HealthNational Human Genome Research InstituteBundesministerium für Bildung und ForschungOffice of ScienceUniversity College LondonBritish Heart FoundationNational Institute for Health and Care ResearchDeutsche ForschungsgemeinschaftU.S. Department of Energy
KeywordsUnified Medical Language SystemAnnotationOntologyDECIPHERControlled vocabularyDocumentationComputer scienceBiologyPhenotypeSet (abstract data type)InteroperabilityUniProtComputational biologyFunction (biology)Information retrievalBioinformaticsWorld Wide WebGenetics

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)0.006
metaresearch head score (Gemma)0.011
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: Software · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0080.009
Science and technology studies0.0010.001
Scholarly communication0.0020.004
Open science0.0020.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.003

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 head0.092
GPT teacher head0.418
Teacher spread0.326 · 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
GenreSoftware

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

Citations837
Published2013
Admission routes1
Has abstractyes

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