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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.

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 3 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.

affno abstractunlabeled
Algorithm Portfolios
Dimitris Souravlias, Konstantinos E. Parsopoulos, Ilias Kotsireas, Pãnos M. Pardalos
2021· book· en· SpringerBriefs in optimization· Computer Science
machine prediction:candidate · noneconsensus · none
7
citations
affunlabeled
Data-driven spatial locality
Svetozar Miučin, Alexandra Fedorova
2018· article· en· Proceedings of the International Symposium on Memory Systems· Computer Science
machine prediction:candidate · noneconsensus · none
7
citations
affno abstractunlabeled
Geometric representations of graphs
Levent Tunçel
2010· book-chapter· en· American Mathematical Society eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
7
citations
affunlabeled
The Future of Graph Analytics
Angela Bonifati, M. TAMER ÖZSU, Yuanyuan Tian, Hannes Voigt, Wenyuan Yu, Wenjie Zhang
2024· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
affno abstractunlabeled
Algorithms and Models for the Web Graph
2023· book· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
affunlabeled
HyGN: Hybrid Graph Engine for NUMA
Tanuj Kr Aasawat, Tahsin Reza, Kazuki Yoshizoe, Matei Ripeanu
2020· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
affunlabeled
An experimental evaluation of giraph and GraphChi
Junnan Lu, Alex Thomo
2016· article· en· 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)· Computer Science
machine prediction:candidate · noneconsensus · none
5
citations
fundno affunlabeled
MillenniumDB: An Open-Source Graph Database System
Domagoj Vrgoč, Carlos Rojas, Renzo Angles, Marcelo Arenas, Diego Arroyuelo, Carlos Buil-Aranda +4 more
2023· article· en· Data Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
5
citations
affunlabeled
Graph Compression for Adjacency-Matrix Multiplication
Alexandre P. Francisco, Travis Gagie, Dominik Köppl, Susana Ladra, Gonzalo Navarro
2022· article· en· SN Computer Science· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
fundno affno abstractunlabeled
EM-type method for measuring graph dissimilarity
Lifei Chen
2013· article· en· International Journal of Machine Learning and Cybernetics· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affunlabeled
Holographic algorithms without matchgates
J. M. Landsberg, Jason Morton, Serguei Norine
2009· preprint· en· ArXiv.org· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affunlabeled
A Roadmap to Graph Analytics
Angela Bonifati, M. TAMER ÖZSU, Yuanyuan Tian, Hannes Voigt, Wenyuan Yu
2025· article· en· ACM SIGMOD Record· Computer Science
machine prediction:candidate · noneconsensus · none
4
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
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
k-core Decomposition on Giraph and GraphChi
Xin Hu, Fangming Liu, Venkatesh Srinivasan, Alex Thomo
2017· book-chapter· en· Lecture notes on data engineering and communications technologies· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations

How this was built: Screen · Findings · About