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4,299,418 works, Canadian by any of four routes.

Every filter state is a URL; the URL is the query; the query is citable via /q/⟨hash⟩. The page, the API and the export parse the same parameters.

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The current cohort, streamed from the database: every work column, the machine labels, the provisional scores, and the per-row validation status. Exports are capped at 100,000 rows. Mints a permanent /q/ link for this exact query. The same filters always produce the same link, whoever asks.

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Semantic Web and Ontologies
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Direct Codex and Gemma labels are unvalidated and sparse. Distilled predictions cover the full frame and are also unvalidated. Choose the evidence source explicitly; absence of a direct label is never a negative label.

affaffiliation
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The four routes compose: require the funder route and exclude affiliation to get the funder-only stratum no affiliation-based frame ever sees.

1,983 results · 1 filter active ·
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20002025
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Machine labels · sparse coverage
Evidence
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
1,983 works in the cohort · of 4,299,418page 30 of 40

Labels cover 1 of 1,983 works in this cohort. The rest are unlabeled, which is not a negative label: the label table is sparse today and grows as labeling rounds land.

Distilled predictions cover 1,983 of 1,983 works in this cohort. Predictions are machine_predicted_unvalidated. The Gemma side is a direct model label for every work (title-only); the Codex side is a distilled, calibrated classifier. Candidate is the union; consensus is the intersection.

affunlabeled
Ut pictura poesis
David Griffin
2013· article· la· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Session details: USEWOD'16
Bettina Berendt, Laura Hollink, Markus Luczak–Roesch
2016· article· en· The Web Conference· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
affunlabeled
Keynotes
M. TAMER ÖZSU, Bio Tamer Özsu
2022· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
GUEST EDITOR'S INTRODUCTION
XIAO-PING ZHANG
2007· article· en· International Journal of Semantic Computing· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Copyright permissions
2003· other· en· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affno abstractunlabeled
Making the Semantic Web Usable for Biologists
Paul M. K. Gordon, Mark D. Wilkinson, Christoph W. Sensen
2008· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Unifying the Fallacies?
John Woods
2004· book-chapter· en· Applied logic series· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
afffundunlabeled
An Ontological Analysis of Water Features
Boyan Brodaric, Torsten Hahmann, Michael Grüninger
2016· article· en· International Conference on GIScience Short Paper Proceedings· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Facilitating and promoting web annotation with Argo
Matthew Shardlow, Piotr Przybyła, Riza Batista-Navarro, Jacob Carter, John McNaught, Sophia Ananiadou
2016· article· en· Research Explorer (The University of Manchester)· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Open Economy Ontology (OEO)
2024· article· en· Zenodo (CERN European Organization for Nuclear Research)· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Wikidata5m - knowledge graph (inductive)
Xiaozhi Wang, Tianyu Gao, Zhaocheng Zhu, Zhengyan Zhang, Zhiyuan Liu, Juanzi Li +1 more
2019· dataset· en· Zenodo (CERN European Organization for Nuclear Research)· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Document Management
Airi Salminen, Frank Wm. Tompa
2011· book-chapter· en· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
venueno affno abstractunlabeled
ISSUE INFORMATION
2017· paratext· en· Hemodialysis International· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
affunlabeled
CALCULER LA SÉMANTIQUE AVEC IEML
Pierre Lévy
2023· preprint· fr· HAL (Le Centre pour la Communication Scientifique Directe)· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Using INMDB for the Semantic Web
Patrick Demers
2017· dissertation· en· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affaboutunlabeled
Capitalising on Metadata: Tool Develoment Plans
Chuck Humphrey
2007· article· en· Zenodo (CERN European Organization for Nuclear Research)· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Fourth annual workshop on data-driven knowledge mobilization.
Ehsan Noei, Kelly Lyons, Eleni Stroulia, Periklis Andritsos
2017· article· en· Conference of the Centre for Advanced Studies on Collaborative Research· Computer Science
machine prediction:candidate · metaresearchconsensus · none
0
citations
affaboutunlabeled
The Calgary Semantic Decision Project
Emiko J. Muraki, Penny M. Pexman
2024· preprint· en· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Preview of the CLA statement on OA
Peter Suber
2008· preprint· en· Computer Science
machine prediction:candidate · scholarly_communication+open_science+insufficient_payloadconsensus · none
0
citations
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