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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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Advanced Clustering Algorithms Research
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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.

421 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.
421 works in the cohort · of 4,299,418page 2 of 9

Labels cover 0 of 421 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 421 of 421 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
Hierarchical Density-Based Clustering Using MapReduce
Joelson Antônio dos Santos, Talat Iqbal Syed, Murilo Coelho Naldi, Ricardo J. G. B. Campello, Jörg Sander
2019· article· en· IEEE Transactions on Big Data· Computer Science
machine prediction:candidate · noneconsensus · none
55
citations
venueno affunlabeled
Estimating the number of clusters using diversity
Suneel Kumar Kingrani, Mark Levene, Dell Zhang
2017· article· en· Artificial Intelligence Research· Computer Science
machine prediction:candidate · noneconsensus · none
51
citations
afffundunlabeled
Weighted Clustering
Margareta Ackerman, Shai Ben-David, Simina Brânzei, David Loker
2021· article· en· Proceedings of the AAAI Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
48
citations
affno abstractunlabeled
Subspace multi-clustering: a review
Juhua Hu, Jian Pei
2017· review· en· Knowledge and Information Systems· Computer Science
machine prediction:candidate · noneconsensus · none
41
citations
affunlabeled
NP-Hardness of Euclidean Sum-of-Squares Clustering
Daniel Aloise, Amit Deshpande, Pierre Hansen, Preyas Popat
2008· article· fr· PolyPublie (École Polytechnique de Montréal)· Computer Science
machine prediction:candidate · noneconsensus · none
39
citations
affunlabeled
Constraint-driven clustering
Rong Ge, Martin Ester, Wen Jin, Ian Davidson
2007· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
38
citations
affno abstractunlabeled
Feature selection using structural similarity
Sushmita Mitra, Partha Pratim Kundu, Witold Pedrycz
2012· article· en· Information Sciences· Computer Science
machine prediction:candidate · noneconsensus · none
33
citations
aboutno affunlabeled
Approximate Clustering Ensemble Method for Big Data
Mohammad Sultan Mahmud, Joshua Zhexue Huang, Rukhsana Ruby, Alladoumbaye Ngueilbaye, Kaishun Wu
2023· article· en· IEEE Transactions on Big Data· Computer Science
machine prediction:candidate · noneconsensus · none
31
citations
affno abstractunlabeled
Flocking based approach for data clustering
Abbas Ahmadi, Fakhri Karray, Mohamed S. Kamel
2009· article· en· Natural Computing· Computer Science
machine prediction:candidate · noneconsensus · none
27
citations
affunlabeled
Type-II Fuzzy Possibilistic C-Mean Clustering
Mohammad Hossein Fazel Zarandi, Marzieh Zarinbal, İ.B. Türkşen
2009· article· en· European Society for Fuzzy Logic and Technology Conference· Computer Science
machine prediction:candidate · noneconsensus · none
26
citations
affunlabeled
Using Pivots to Speed-Up k-Medoids Clustering
Adriano Arantes Paterlini, Mário A. Nascimento, Caetano Traina
2011· article· en· Cadernos de Linguística e Teoria da Literatura (Universidade Federal de Minas Gerais)· Computer Science
machine prediction:candidate · noneconsensus · none
24
citations
affno abstractunlabeled
Finding multiple stable clusterings
Juhua Hu, Qi Qian, Jian Pei, Rong Jin, Shenghuo Zhu
2016· article· en· Knowledge and Information Systems· Computer Science
machine prediction:candidate · noneconsensus · none
23
citations
venueno affunlabeled
Parallelization of the K-Means++ Clustering Algorithm
Sara Daoudi, Chakib Mustapha Anouar Zouaoui, Miloud Chikr El-Mezouar, Nasreddine Taleb
2021· article· en· Ingénierie des systèmes d information· Computer Science
machine prediction:candidate · noneconsensus · none
22
citations
affunlabeled
Document clustering with committees
Patrick Pantel, Dekang Lin
2002· article· en· Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '02· Computer Science
machine prediction:candidate · noneconsensus · none
22
citations

How this was built: Screen · Findings · About