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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
Evidence
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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 10 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.

affno abstractunlabeled
An Associative Classifier for Uncertain Datasets
Metanat Hooshsadat, Osmar R. Zai͏̈ane
2012· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
7
citations
affno abstractunlabeled
Searching for a Black Hole in Tree Networks
Jurek Czyzowicz, Dariusz R. Kowalski, Ευριπίδης Μάρκου, Andrzej Pelc
2005· article· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
7
citations
afffundunlabeled
Towards Cohesive Anomaly Mining
Yun Xiong, Yangyong Zhu, Philip S. Yu, Jian Pei
2013· article· en· Proceedings of the AAAI Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
7
citations
afffundunlabeled
Detection of rare items with TARGET
Guangzhe Fan, Mu Zhu
2011· article· en· Statistics and Its Interface· Computer Science
machine prediction:candidate · noneconsensus · none
7
citations
afffundunlabeled
Mining Contextual Item Similarity without Concept Hierarchy
Md. Fahim Arefin, Chowdhury Farhan Ahmed, Redwan Ahmed Rizvee, Carson K. Leung, Longbing Cao
2022· article· en· 2022 16th International Conference on Ubiquitous Information Management and Communication (IMCOM)· Computer Science
machine prediction:candidate · noneconsensus · none
7
citations
affno abstractunlabeled
Learning Statistically Significant Contrast Sets
Mohomed Shazan Mohomed Jabbar, Osmar R. Zai͏̈ane
2016· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
affunlabeled
Educational Data Mining
2015· reference-entry· en· The SAGE Encyclopedia of Educational Technology· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
affno abstractunlabeled
ICS: An Interactive Classification System
Yan Zhao, Yiyu Yao, Mingwu Yan
2007· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
affunlabeled
An apples-to-apples comparison of two database journals
Philip A. Bernstein, Elisa Bertino, Andreas Heuer, Christian S. Jensen, Holger Meyer, M. TAMER ÖZSU +2 more
2005· article· en· ACM SIGMOD Record· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
affno abstractunlabeled
Mining Icebergs in Time-Stamped Databases.
Jhimli Adhikari, P.R. Rao, Witold Pedrycz
2011· article· en· Indian International Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
affunlabeled
Interactive Visual Analytics of Databases and Frequent Sets
Carson K. Leung, Christopher L. Carmichael, Patrick Johnstone, David Sonny Hung-Cheung Yuen
2013· article· en· International Journal of Information Retrieval Research· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
affno abstractunlabeled
Mining Partial Orders from Sequences
Guozhu Dong, Jian Pei
2007· book-chapter· en· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
fundno affno abstractunlabeled
Machine Learning and Knowledge Discovery in Databases
Massih-Réza Amini, Stéphane Canu, Asja Fischer, Tias Guns, Petra Kralj Novak, Grigorios Tsoumakas
2023· book· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
affunlabeled
DiMaC
Hua Ming, Jian Pei
2008· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
venueno affno abstractunlabeled
The K-behaviour of p-trees.
Marco Liverani, Aurora Morgana, Célia Picinin de Mello
2007· article· en· Ars Combinatoria· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations

How this was built: Screen · Findings · About