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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 ·
Categories
Machine labels · sparse coverage
Evidence
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 1 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 teacher distillation outputs. Candidate is the union; consensus is the intersection.

afffundunlabeled
Privacy-preserving data publishing
2010· review· en· ACM Computing Surveys· Computer Science
distilled prediction:candidate · metaresearch+metaepi_narrow+scholarly_communication+open_science+research_integrityconsensus · open_science+research_integrity
1,639
citations
afffundunlabeled
Federated Learning for Smart Healthcare: A Survey
2022· review· en· ACM Computing Surveys· Computer Science
distilled prediction:candidate · metaresearch+metaepi_narrow+sts+open_science+research_integrityconsensus · open_science
733
citations
afffundunlabeled
Personalized Cross-Silo Federated Learning on Non-IID Data
2021· article· en· Proceedings of the AAAI Conference on Artificial Intelligence· Computer Science
distilled prediction:candidate · metaresearch+metaepi_narrow+scholarly_communication+open_scienceconsensus · open_science
659
citations
affunlabeled
(α, k)-anonymity
2006· article· en· Computer Science
distilled prediction:candidate · open_scienceconsensus · open_science
628
citations
affunlabeled
k-automorphism
2009· article· en· Proceedings of the VLDB Endowment· Computer Science
distilled prediction:candidate · open_scienceconsensus · open_science
407
citations
affunlabeled
Protecting Privacy Using k-Anonymity
2008· article· en· Journal of the American Medical Informatics Association· Computer Science
distilled prediction:candidate · metaresearch+open_scienceconsensus · open_science
334
citations
affunlabeled
Anonymizing sequential releases
2006· article· en· Computer Science
distilled prediction:candidate · open_scienceconsensus · open_science
257
citations
affno abstractunlabeled
PySyft: A Library for Easy Federated Learning
2021· book-chapter· en· Studies in computational intelligence· Computer Science
distilled prediction:candidate · metaresearch+metaepi_narrow+open_scienceconsensus · open_science
208
citations

How this was built: Screen · Findings · About