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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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Advanced Graph Neural Networks
Retraction
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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.

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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.

518 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.
518 works in the cohort · of 4,299,418page 9 of 11

Labels cover 2 of 518 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 518 of 518 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.

affno abstractunlabeled
Social Graph Dataset
Professor Reda Alhajj, Professor Jon Rokne
2014· book-chapter· en· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Multi-Relational Data and Knowledge Graphs
William L. Hamilton
2020· book-chapter· en· Synthesis lectures on artificial intelligence and machine learning· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
InductiveQE Datasets
2022· article· en· Greater South Information System· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Open Problems
Matthew Guzdial, Sam Snodgrass, Adam Summerville
2025· book-chapter· en· Synthesis lectures on games and computational intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Occurrence Download
2018· dataset· en· Global Biodiversity Information Facility· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affno abstractunlabeled
Preliminaries
Tek Raj Chhetri
2025· book-chapter· en· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affno abstractunlabeled
Classification of Actors in Social Networks Using RLVECO
Bonaventure C. Molokwu, Shaon Bhatta Shuvo, Narayan C. Kar, Ziad Kobti
2020· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
A Simple Latent Variable Model for Graph Learning and Inference
Manfred Jaeger, Antonio Longa, Steve Azzolin, Oliver Schulte, Andrea Passerini
2023· article· en· Institutional Research Information System (Università degli Studi di Trento)· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
An Empirical Study of Retrieval-Enhanced Graph Neural Networks
Dingmin Wang, Shengchao Liu, Hanchen Wang, Bernardo Cuenca Grau, Linfeng Song, Jian Tang +2 more
2023· book-chapter· en· Frontiers in artificial intelligence and applications· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
SESYNC-ci/gis-abm-lesson: Handouts - Zenodo
2021· other· fr· Zenodo (CERN European Organization for Nuclear Research)· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
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How this was built: Screen · Findings · About