MétaCan
Menu
Back to cohort
Record W4245816202 · doi:10.5596/c07-035

Web 3.0 and health librarians: an introduction

2008· article· fr· W4245816202 on OpenAlexaffvenue
Allan Cho

Bibliographic record

VenueJournal of the Canadian Health Libraries Association / Journal de l Association de bilbiothèques de la santé du Canada · 2008
Typearticle
Languagefr
FieldBiochemistry, Genetics and Molecular Biology
TopicBiomedical Text Mining and Ontologies
Canadian institutionsStornoway Diamond (Canada)
Fundersnot available
KeywordsWorld Wide WebSocial Semantic WebSemantic WebComputer scienceWeb standardsData WebWeb modelingWeb developmentWeb intelligenceSemantic Web StackWeb designWeb pageTheme (computing)

Abstract

fetched live from OpenAlex

Key messages• Web 3.0 refers to the third decade of the Web from 2010-2020.Some experts believe we are entering a pre-Web 3.0 period.• The current Web is characterized by global information overload and repetitive searching and browsing using Google.• Debates about Web 3.0 are still somewhat theoretical, but a common theme is "developing an integrated web of data" based on sound principles of information systems design.Some experts say that the principles of librarianship should play a role in improving how the Web is organized.• In 2008, semantic technologies are being used to solve information retrieval problems in bioinformatics, which may have specific applications in medicine.The term "Semantic Web" is occasionally used as a synonym for Web 3.0 (and vice versa), though some disagree with that usage.• Health librarians should be thinking ahead about how to design better domain-specific search tools and user experiences (including virtual) in Web 3.0.

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)0.003
metaresearch head score (Gemma)0.004
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: Review · Consensus signal: Review
Teacher disagreement score0.020
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.013
Science and technology studies0.0030.005
Scholarly communication0.0100.017
Open science0.0010.004
Research integrity0.0090.007
Insufficient payload (model declined to judge)0.0200.007

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 head0.009
GPT teacher head0.250
Teacher spread0.242 · 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
GenreReview

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
Published2008
Admission routes2
Has abstractyes

Explore more

Same venueJournal of the Canadian Health Libraries Association / Journal de l Association de bilbiothèques de la santé du CanadaSame topicBiomedical Text Mining and OntologiesFrench-language works237,207