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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.

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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 23 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
Knowledge discovery in a circle of trust
Liam Peyton, Jun Hu
2007· article· en· WIT transactions on information and communication technologies· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Privacy-Preserving Data Mining on the Web
Stanley R. Oliveira, Osmar R. Zai͏̈ane
2006· book-chapter· en· IGI Global eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Bridging the trust gaps in biometrics
Samuel M. Curtis, Delfina Belli, Sacha Alanoca, Adriana Bora, Nicolas Miailhe, Yolanda Lannquist
2021· article· en· Biometric Technology Today· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
DPack: Efficiency-Oriented Privacy Budget Scheduling
Pierre Tholoniat, Kelly Kostopoulou, Mosharaf Chowdhury, Asaf Cidon, Roxana Geambasu, Mathias Lécuyer +1 more
2025· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affaboutunlabeled
Mathematics, risk, and messy survey data
Kristi Thompson, Carolyn Sullivan
2020· article· en· IASSIST Quarterly· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
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How this was built: Screen · Findings · About