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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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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.

849 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.
849 works in the cohort · of 4,299,418page 2 of 17

Labels cover 1 of 849 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 849 of 849 works in this cohort. Predictions are machine_predicted_unvalidated teacher distillation outputs. Candidate is the union; consensus is the intersection.

afffundunlabeled
A Goal‐based Classification of Web Information Tasks
Melanie Kellar, Carolyn Watters, Michael Shepherd
2006· article· en· Proceedings of the American Society for Information Science and Technology· Computer Science
distilled prediction:candidate · noneconsensus · none
79
citations
affno abstractunlabeled
A recommender system of reviewers and experts in reviewing problems
Jarosław Protasiewicz, Witold Pedrycz, Marek Kozłowski, Sławomir Dadas, Tomasz Stanisławek, Agata Kopacz +1 more
2016· article· en· Knowledge-Based Systems· Computer Science
distilled prediction:candidate · noneconsensus · none
75
citations
affunlabeled
Low-Rank Linear Cold-Start Recommendation from Social Data
Suvash Sedhain, Aditya Krishna Menon, Scott Sanner, Lexing Xie, Darius Braziunas
2017· article· en· Proceedings of the AAAI Conference on Artificial Intelligence· Computer Science
distilled prediction:candidate · open_scienceconsensus · none
72
citations
affno abstractunlabeled
Social Recommender Systems
Reda Alhajj, Jon Rokne
2018· book-chapter· en· Computer Science
distilled prediction:candidate · metaepi_narrow+insufficient_payloadconsensus · none
71
citations
affno abstractunlabeled
Towards context-sensitive collaborative media recommender system
Mohammed F. Alhamid, Majdi Rawashdeh, Hussein Al Osman, M. Shamim Hossain, Abdulmotaleb El Saddik
2014· article· en· Multimedia Tools and Applications· Computer Science
distilled prediction:candidate · noneconsensus · none
70
citations
affno abstractunlabeled
Learning a Model of a Web User’s Interests
Tingshao Zhu, Russell Greiner, Gerald Häubl
2003· book-chapter· en· Lecture notes in computer science· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
66
citations
afffundunlabeled
Recommending user generated item lists
Yidan Liu, Min Xie, Laks V. S. Lakshmanan
2014· article· en· Computer Science
distilled prediction:candidate · noneconsensus · none
49
citations
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