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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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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 14 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.

afffundunlabeled
Standardizing interestingness measures for association rules
Mateen Shaikh, Paul D. McNicholas, Maria-Luiza Antonie, Thomas Brendan Murphy
2018· preprint· en· Statistical Analysis and Data Mining The ASA Data Science Journal· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Generating Relational Database using Ontology Review
Christina Khnaisser, Luc Lavoie, Anita Burgun, Jean‐François Éthier
2018· article· en· International Journal of Advanced Computer Science and Applications· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
venueno affunlabeled
A New Mood’s Median Test for Imprecise Data
Abdulrahman AlAita, Muhammad Ahtisham Aslam, Florentín Smarandache
2024· article· en· International Journal of Analysis and Applications· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Frequent Itemsets as Descriptors of Textual Records
Ayoub Bokhabrine, Ismaïl Biskri, Nadia Ghazzali
2019· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
High-Utility Interval-Based Sequences
2020· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Convertible Constraints
Carson K. Leung
2009· book-chapter· en· Encyclopedia of Database Systems· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
fundno affno abstractunlabeled
Mathematics and Computing 2013
R. N. Mohapatra, P. K. Saxena, Prakash Srivastava
2014· book· en· Springer proceedings in mathematics & statistics· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
A Data Mining Technique
Robert J. Hilderman, Howard J. Hamilton
2001· book-chapter· en· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
The secret behind the Luhn-ie
Broderick Causley
2012· article· en· XRDS Crossroads The ACM Magazine for Students· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Trees
Matthias Schonlau
2023· book-chapter· en· Statisctics and computing/Statistics and computing· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
fundno affunlabeled
Knowledge Discovery in Databases and Decision Support
Anantha Mahadevan, Kumudini Ponnudurai, Gregory E. Kersten, Roland Thomas
2006· book-chapter· en· Kluwer Academic Publishers eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
INDEPENDENT DE-DUPLICATION IN DATA CLEANING
Ajumobi Udechukwu, C. I. Ezeife, Ken Barker
2005· article· en· University of Zagreb University Computing Centre (SRCE)· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Introduction to ACM multimedia 2010 best paper candidates
Shervin Shirmohammadi, Jiebo Luo, Jie Yang, Abdulmotaleb El Saddik
2011· article· en· ACM Transactions on Multimedia Computing Communications and Applications· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
1
citations

How this was built: Screen · Findings · About