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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 4 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
Tensor Recurrent Neural Network With Differential Privacy
Jun Feng, Laurence T. Yang, Bocheng Ren, Deqing Zou, Mianxiong Dong, Shunli Zhang
2023· article· en· IEEE Transactions on Computers· Computer Science
machine prediction:candidate · noneconsensus · none
64
citations
affno abstractunlabeled
Privacy-preserving boosting
Sébastien Gambs, Balázs Kégl, Esma Aı̈meur
2007· article· en· Data Mining and Knowledge Discovery· Computer Science
machine prediction:candidate · noneconsensus · none
60
citations
affno abstractunlabeled
Publishing anonymous survey rating data
Xiaoxun Sun, Hua Wang, Jiuyong Li, Jian Pei
2010· article· en· Data Mining and Knowledge Discovery· Computer Science
machine prediction:candidate · metaresearchconsensus · none
56
citations
affunlabeled
Notes on information-theoretic privacy
Shahab Asoodeh, Fady Alajaji, Tamás Linder
2014· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
55
citations
affunlabeled
Privacy-preserving heterogeneous health data sharing
Noman Mohammed, Xiaoqian Jiang, Rui Chen, Benjamin C. M. Fung, Lucila Ohno‐Machado
2012· article· en· Journal of the American Medical Informatics Association· Computer Science
machine prediction:candidate · noneconsensus · none
55
citations
affunlabeled
SAQE
Johes Bater, Yongjoo Park, Xi He, Xiao Wang, Jennie Rogers
2020· article· en· Proceedings of the VLDB Endowment· Computer Science
machine prediction:candidate · noneconsensus · none
53
citations
affunlabeled
Asynchronous Federated Unlearning
Ningxin Su, Baochun Li
2023· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
51
citations
afffundno abstractunlabeled
Anonymizing trajectory data for passenger flow analysis
Moein Ghasemzadeh, Benjamin C. M. Fung, Rui Chen, Anjali Awasthi
2014· article· en· Transportation Research Part C Emerging Technologies· Computer Science
machine prediction:candidate · noneconsensus · none
50
citations
affunlabeled
Synthetic Data for Social Good
Bill Howe, Julia Stoyanovich, Haoyue Ping, Bernease Herman, Matt Gee
2017· preprint· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · open_scienceconsensus · none
50
citations
affno abstractunlabeled
Exploring privacy measurement in federated learning
Gopi Krishna Jagarlamudi, Abbas Yazdinejad, Reza M. Parizi, Seyedamin Pouriyeh
2023· article· en· The Journal of Supercomputing· Computer Science
machine prediction:candidate · noneconsensus · none
49
citations

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