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Record W2966344753 · doi:10.21105/joss.01182

VIVO: a system for research discovery

2019· article· es· W2966344753 on OpenAlexaff
Michael Conlon, Andrew Woods, Graham Triggs, Ralph O’Flinn, Muhammad Javed, Jim Blake, M. Benjamin Gross, Qazi Asim Ijaz Ahmad, Sabih Ali, Martin Barber, Don Elsborg, Kitio Fofack, Christian Hauschke, Violeta Ilik, Huda Khan, Ted Lawless, Jacob Levernier, Brian Lowe, Jose Martin, Steve Adkins McKay, Simon Porter, Tatiana Walther, Marijane White, Stefan Wolff, Rebecca Younes

Bibliographic record

VenueThe Journal of Open Source Software · 2019
Typearticle
Languagees
FieldComputer Science
TopicSemantic Web and Ontologies
Canadian institutionsUniversité du Québec à Montréal
FundersNational Institutes of Health
KeywordsIn vivoComputational biologyData scienceComputer scienceBiologyBiotechnology

Abstract

fetched live from OpenAlex

vee-voh] is member-supported, enterprise open source software and an ontology for representing scholarship. VIVO supports recording, editing, searching, browsing and visualizing scholarly activity. VIVO encourages research discovery, expert finding, network analysis and assessment of research impact. VIVO is easily extended to support additional domains of scholarly activity (Brner, Conlon, Corson-Rikert, & Ying Ding, 2012).

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)0.005
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Software · Consensus signal: Software
Teacher disagreement score0.992
Threshold uncertainty score0.146

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.014
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0080.007
Science and technology studies0.0020.001
Scholarly communication0.0080.011
Open science0.0040.008
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0440.039

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 head0.084
GPT teacher head0.374
Teacher spread0.290 · 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.

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

Citations17
Published2019
Admission routes1
Has abstractyes

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