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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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Peer-to-Peer Network Technologies
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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.

880 results · 1 filter active ·
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20002025
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Machine labels · sparse coverage
Evidence
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
880 works in the cohort · of 4,299,418page 3 of 18

Labels cover 1 of 880 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 880 of 880 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
Historic data access in publish/subscribe
Li Guo, Alex King Yeung Cheung, Sh. Hou, Songlin Hu, Vinod Muthusamy, Reza Sherafat +3 more
2007· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
30
citations
afffundunlabeled
Personal volunteer computing
Erick Lavoie
2020· preprint· en· Open MIND· Computer Science
machine prediction:candidate · noneconsensus · none
25
citations
affno abstractunlabeled
Supporting Peer-to-Peer User Communities
Julita Vassileva
2002· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
24
citations
affno abstractunlabeled
The JXTA performance model and evaluation
Emir Halepovic, Ralph Deters
2004· article· en· Future Generation Computer Systems· Computer Science
machine prediction:candidate · noneconsensus · none
22
citations
affno abstractunlabeled
Web 2.0 OLAP: From Data Cubes to Tag Clouds
Kamel Aouiche, Daniel Lemire, Robert Godin
2009· book-chapter· en· Lecture notes in business information processing· Computer Science
machine prediction:candidate · noneconsensus · none
22
citations
affunlabeled
Cooperation strategies for agent-based P2P systems
Loris Penserini, Lin Liu, John Mylopoulos, Maurizio Panti, Luca Spalazzi
2003· article· en· Università Politecnica delle Marche (Università Politecnica delle Marche)· Computer Science
machine prediction:candidate · noneconsensus · none
22
citations
afffundunlabeled
Personal volunteer computing
2019· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
21
citations
affunlabeled
Peer-to-Peer Mobility Management for all-IP Networks
Ramy Farha, Khashayar Khavari, Nadeem Abji, Alberto Leon‐Garcia
2006· article· en· 2006 IEEE International Conference on Communications· Computer Science
machine prediction:candidate · noneconsensus · none
21
citations
affunlabeled
Maintaining replicas in unstructured P2P systems
Christof Leng, Wesley W. Terpstra, Bettina Kemme, Wilhelm Stannat, Alejandro Buchmann
2008· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
21
citations
affunlabeled
Predictive publish/subscribe matching
Vinod Muthusamy, Haifeng Liu, Hans‐Arno Jacobsen
2010· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
20
citations
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