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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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Graph Theory and Algorithms
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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.

423 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.
423 works in the cohort · of 4,299,418page 4 of 9

Labels cover 2 of 423 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 423 of 423 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
Tree structured data processing on GPUs
Yifan Lu, Lü Yang, Virendrakumar C. Bhavsar, Neetesh Kumar
2017· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affunlabeled
Strahler based graph clustering using convolution
David Auber, Yves Chiricota
2004· article· en· Proceedings. Eighth International Conference on Information Visualisation, 2004. IV 2004.· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affunlabeled
Dynamic Graph Summarization: Optimal and Scalable
Mahdi Hajiabadi, Venkatesh Srinivasan, Alex Thomo
2022· article· en· 2022 IEEE International Conference on Big Data (Big Data)· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
PageRank for Billion-Scale Networks in RDBMS
Aly Ahmed, Alex Thomo
2020· book-chapter· en· Advances in intelligent systems and computing· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
afffundno abstractunlabeled
Generalizing Lloyd’s Algorithm for Graph Clustering
Tareq Uz Zaman, Nicolas Nytko, Ali Taghibakhshi, Scott MacLachlan, Luke N. Olson, Matthew West
2024· article· en· SIAM Journal on Scientific Computing· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
venueno affno abstractunlabeled
On clique graph recognition.
Marisa Gutiérrez, João Meidânis
2002· article· en· Ars Combinatoria· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
Superstochastic matrices and magic Markov chains
Karl Gustafson, George P. H. Styan
2009· article· en· Linear Algebra and its Applications· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
On the continuity of graph parameters
Matthew Hurshman, Jeannette Janssen
2014· article· en· Discrete Applied Mathematics· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Position paper
Hassan H. Halawa, Matei Ripeanu
2021· article· en· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
2
citations
affno abstractunlabeled
Sequential Graph Matching with Sequential Monte Carlo.
Seong-Hwan Jun, Samuel W. K. Wong, James V. Zidek, Alexandre Bouchard‐Côté
2017· article· en· International Conference on Artificial Intelligence and Statistics· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affaboutunlabeled
TGDB: towards a benchmark for graph databases
Zahid Abul-Basher, Mark Chignell, Parke Godfrey, Nikolay Yakovets
2016· article· en· Computer Science and Software Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Grafs: declarative graph analytics
Farzin Houshmand, Mohsen Lesani, Keval Vora
2021· article· en· Proceedings of the ACM on Programming Languages· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
aboutno affunlabeled
Streaming METIS partitioning
Ghizlane Echbarthi, Hamamache Kheddouci
2016· preprint· en· 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Using graphs and charts in scientific figures
Karol Buda, Kateřina Čermáková, H. Courtney Hodges, Eugenio F. Fornasiero, Shahar Sukenik, Alex S. Holehouse
2023· article· en· Trends in Biochemical Sciences· Computer Science
machine prediction:candidate · metaresearchconsensus · none
2
citations
fundno affunlabeled
MASTIFF
Mohsen Koohi Esfahani, Peter Kilpatrick, Hans Vandierendonck
2022· article· en· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
2
citations
affunlabeled
Robust Neural Model for Searching over Incomplete Graphs
Radin Hamidi Rad, Ebrahim Bagheri, Mehdi Kargar, Divesh Srivastava, Jaroslaw Szlichta
2025· article· en· ACM Transactions on Intelligent Systems and Technology· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
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How this was built: Screen · Findings · About