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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 27 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
The Tracking Machine Learning challenge : Throughput phase
Sabrina Amrouche, L. Basara, P. Calafiura, D. Emeliyanov, Victor Estrade, Steven Farrell +13 more
2021· preprint· en· OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information)· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Privacy FP-Tree
Sampson Pun, Ken Barker
2009· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Data Sovereignty
Melissa Lukings, Arash Habibi Lashkari
2022· book-chapter· en· Progress in IS· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Robust Federated Learning by Mixture of Experts
Saeedeh Parsaeefard, Sayed Ehsan Etesami, Alberto Leon‐Garcia
2021· preprint· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
AI Security and Privacy
Dilli Prasad Sharma, Arash Habibi Lashkari, Mahdi Daghmehchi Firoozjaei, Samaneh Mahdavifar, Pulei Xiong
2025· book-chapter· en· Progress in IS· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Differential-Privacy Capacity
Wael Alghamdi, Shahab Asoodeh, Flávio P. Calmon, Oliver Kosut, Lalitha Sankar
2024· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
A Welfare Test for Sharing Health Data
Anindya Sen, Helen Chen, Maura R. Grossman, Shu‐Feng Tsao
2024· article· en· Review of Income and Wealth· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations

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