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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.

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.

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 30 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.

venueno affunlabeled
Using Deep Learning to Block Web Tracking
Jianyi Wang
2020· article· en· Computer and Information Science· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Editorial: Articles, perspectives, and TPDP
Lars Vilhuber
2021· editorial· en· DOAJ (DOAJ: Directory of Open Access Journals)· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Privacy Framework for Open Environments
Afshan Samani, Adrian T. Beinkowski, Raafat Aburukba, Hamada Ghenniwa
2013· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Anonymized Data
Reda Alhajj, Jon Rokne
2018· book-chapter· en· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Basic Differentially Private Mechanisms
Jérôme Le Ny
2020· book-chapter· en· Springer briefs in electrical and computer engineering· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Privacy in Network Systems
Jérôme Le Ny
2020· book-chapter· en· Encyclopedia of Systems and Control· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Data Mining and Privacy
Esma Aı̈meur, Sébastien Gambs
2013· book-chapter· en· IGI Global eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Differentially Private Clustering Algorithm for Mixed Data
Kai Cheng, Liandong Chen, Huifeng Yang, Dan Luo, Shuai Yuan, Zhitao Guan
2023· book-chapter· en· Communications in computer and information science· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations

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