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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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Data Mining Algorithms and Applications
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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.

1,100 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.
1,100 works in the cohort · of 4,299,418page 6 of 22

Labels cover 1 of 1,100 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 1,100 of 1,100 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
WEBKDD 2002
Brij Masand, Myra Spiliopoulou, Jaideep Srivastava, Osmar R. Zai͏̈ane
2002· article· en· ACM SIGKDD Explorations Newsletter· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
19
citations
affno abstractunlabeled
A new tree-based approach to mine sequential patterns
Redwan Ahmed Rizvee, Chowdhury Farhan Ahmed, Md. Fahim Arefin, Carson K. Leung
2023· article· en· Expert Systems with Applications· Computer Science
machine prediction:candidate · noneconsensus · none
18
citations
affunlabeled
Fuzzy joins in MapReduce
Ben Kimmett, Venkatesh Srinivasan, Alex Thomo
2015· article· en· Proceedings of the VLDB Endowment· Computer Science
machine prediction:candidate · noneconsensus · none
17
citations
affno abstractunlabeled
Multi-level Frequent Pattern Mining
Todd Eavis, Xi Zheng
2009· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
16
citations
affno abstractunlabeled
Interactive Construction of Decision Trees
Jianchao Han, Nick Cercone
2001· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
16
citations
affno abstractunlabeled
Mining Social Networks for Significant Friend Groups
Carson K. Leung, Syed Khairuzzaman Tanbeer
2012· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
16
citations
affunlabeled
Mining Patterns That Respond to Actions
Yuelong Jiang, Ke Wang, Alexander Tuzhilin, Ada Wai-Chee Fu
2006· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
16
citations
venueno affunlabeled
Extraction of association rules using big data technologies
Carlos Fernandez‐Basso, M. Dolores Ruiz, Marı́a J. Martı́n-Bautista
2016· article· en· International Journal of Design & Nature and Ecodynamics· Computer Science
machine prediction:candidate · noneconsensus · none
15
citations
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