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Record W3181498676 · doi:10.1093/database/baab040

Standardization of assay representation in the Ontology for Biomedical Investigations

2021· review· en· W3181498676 on OpenAlexaff
Randi Vita, Jie Zheng, Rebecca Jackson, Damion Dooley, James A. Overton, Mark A. Miller, Daniel C. Berrios, Richard H. Scheuermann, Yongqun He, Hande Küçük McGinty, Mathias Brochhausen, Aisyah Yu Lin, Sagar B Jain, Marcus C. Chibucos, J. Judkins, Michelle Giglio, Irene Y Feng, Gully Burns, Matthew Brush, Bjoern Peters, Christian J. Stoeckert

Bibliographic record

VenueDatabase · 2021
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBiomedical Text Mining and Ontologies
Canadian institutionsSimon Fraser University
FundersNational Institute of Allergy and Infectious DiseasesNational Human Genome Research InstituteBill and Melinda Gates FoundationNational Institutes of HealthNational Science Foundation
KeywordsStandardizationOntologyComputer scienceProcess (computing)Open Biomedical OntologiesSoftware engineeringRepresentation (politics)HierarchyProcess ontologyInformation retrievalOntology Inference LayerOWL-STerm (time)World Wide WebSemantic WebSuggested Upper Merged OntologyProgramming languageSemantic Web Stack

Abstract

fetched live from OpenAlex

The Ontology for Biomedical Investigations (OBI) underwent a focused review of assay term annotations, logic and hierarchy with a goal to improve and standardize these terms. As a result, inconsistencies in W3C Web Ontology Language (OWL) expressions were identified and corrected, and additionally, standardized design patterns and a formalized template to maintain them were developed. We describe here this informative and productive process to describe the specific benefits and obstacles for OBI and the universal lessons for similar projects.

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 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)0.015
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0070.007
Science and technology studies0.0010.002
Scholarly communication0.0040.006
Open science0.0030.003
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0020.002

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 head0.119
GPT teacher head0.430
Teacher spread0.311 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations10
Published2021
Admission routes1
Has abstractyes

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