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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 24 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.

affno abstractunlabeled
Towards Robust End-to-End Alignment.
Lê Nguyên Hoang
2019· article· en· National Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Privacy-Preserving Data Publishing
Alip Mohammed, Benjamin C. M. Fung
2022· book-chapter· en· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Big Graph Privacy
Hessam Zakerzadeh, Charų C. Aggarwal, Ken Barker
2015· article· en· EDBT/ICDT Workshops· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Privacy Protection in Geolocation Monitoring Applications
Kush Patel, Krishna Ashutoshbhai Vyas, Monika Patel, Dhruv Vyas, Sergey Butakov
2022· book-chapter· en· Communications in computer and information science· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Security, Privacy, and Applications in Mobile Healthcare
Chien-Lung Hsu, Atsuo Inomata, Sk. Md. Mizanur Rahman, Fatos Xhafa, Laurence T. Yang
2015· article· en· International Journal of Distributed Sensor Networks· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Computing Join Aggregates Over Private Tables
Rong She, Ke Want, Ada Wai-Chee Fu, Yabo Xu
2008· article· en· International Journal of Data Warehousing and Mining· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Privacy Attacks on Schedule-Driven Data
Stephan A. Fahrenkrog-Petersen, Arik Senderovich, Alexandra Tichauer, Ali Kaan Tutak, J. Christopher Beck, Matthias Weidlich
2023· article· en· Proceedings of the AAAI Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Privacy-Enhancing Technologies for Federated Learning
Zahra Batool, Baturalp Buyukates, Reza Nourmohammadi, Kaiwen Zhang
2025· book-chapter· en· Studies in computational intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations

How this was built: Screen · Findings · About