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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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Privacy-Preserving Technologies in Data
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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.

1,809 results · 1 filter active ·
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20002025
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Machine labels · sparse coverage
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
1,809 works in the cohort · of 4,299,418page 3 of 37

Labels cover 7 of 1,809 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 1,809 of 1,809 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
Differential privacy in health research: A scoping review
Joseph Ficek, Wei Wang, Henian Chen, Getachew Dagne, Ellen M. Daley
2021· review· en· Journal of the American Medical Informatics Association· Computer Science
machine prediction:candidate · metaresearchconsensus · none
93
citations
afffundunlabeled
VeriPlace
Wanying Luo, Urs Hengartner
2010· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
90
citations
affunlabeled
Balancing Open Science and Data Privacy in the Water Sciences
Samuel C. Zipper, Kaitlin Stack Whitney, Jillian M. Deines, Kevin M. Befus, Udit Bhatia, Sam Albers +7 more
2019· article· en· Water Resources Research· Computer Science
machine prediction:candidate · open_scienceconsensus · none
86
citations
affunlabeled
PrivFL
Kalikinkar Mandal, Guang Gong
2019· preprint· en· Computer Science
machine prediction:candidate · noneconsensus · none
85
citations
affunlabeled
Dealer
Jinfei Liu, Jian Lou, Junxu Liu, Li Xiong, Jian Pei, Jimeng Sun
2021· article· en· Proceedings of the VLDB Endowment· Computer Science
machine prediction:candidate · noneconsensus · none
82
citations
afffundunlabeled
ARCANE: An Efficient Architecture for Exact Machine Unlearning
Haonan Yan, Xiaoguang Li, Ziyao Guo, Hui Li, Fenghua Li, Xiaodong Lin
2022· article· en· Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
80
citations
affno abstractunlabeled
Blockchain Enabled Privacy Audit Logs
Andrew Sutton, Reza Samavi
2017· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
77
citations
affno abstractunlabeled
Adaptive privacy-preserving federated learning
Xiaoyuan Liu, Hongwei Li, Guowen Xu, Rongxing Lu, Miao He
2020· article· en· Peer-to-Peer Networking and Applications· Computer Science
machine prediction:candidate · noneconsensus · none
72
citations
affunlabeled
Walking in the crowd
Noman Mohammed, Benjamin C. M. Fung, Mourad Debbabi
2009· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
72
citations

How this was built: Screen · Findings · About