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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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Anomaly Detection Techniques 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.

1,158 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.
1,158 works in the cohort · of 4,299,418page 1 of 24

Labels cover 1 of 1,158 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 1,158 of 1,158 works in this cohort. Predictions are machine_predicted_unvalidated teacher distillation outputs. Candidate is the union; consensus is the intersection.

affno abstractunlabeled
Learning Deep Architectures for AI
Yoshua Bengio
2009· article· en· Foundations and Trends® in Machine Learning· Computer Science
distilled prediction:candidate · noneconsensus · none
6,910
citations
affunlabeled
LOF
Markus Breunig, Hans‐Peter Kriegel, Raymond T. Ng, Jörg Sander
2000· article· en· ACM SIGMOD Record· Computer Science
distilled prediction:candidate · noneconsensus · none
5,181
citations
affunlabeled
LOF
Markus Breunig, Hans‐Peter Kriegel, Raymond T. Ng, Jörg Sander
2000· article· en· Computer Science
distilled prediction:candidate · insufficient_payloadconsensus · none
3,862
citations
affno abstractunlabeled
Introduction to Machine Learning
F. Richard Yu, Ying He
2019· book-chapter· en· Springer briefs in electrical and computer engineering· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
1,620
citations
affno abstractunlabeled
A survey on deep learning for big data
Qingchen Zhang, Laurence T. Yang, Zhikui Chen, Peng Li
2017· article· en· Information Fusion· Computer Science
distilled prediction:candidate · noneconsensus · none
1,216
citations
affunlabeled
On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, Kilian Q. Weinberger
2017· article· en· International Conference on Machine Learning· Computer Science
distilled prediction:candidate · noneconsensus · none
1,185
citations
affunlabeled
Analysis of Dimensionality Reduction Techniques on Big Data
Thippa Reddy Gadekallu, Praveen Kumar Reddy Maddikunta, Kuruva Lakshmanna, Rajesh Kaluri, Dharmendra Singh Rajput, Gautam Srivastava +1 more
2020· article· en· IEEE Access· Computer Science
distilled prediction:candidate · noneconsensus · none
869
citations
affno abstractunlabeled
A survey on machine learning for data fusion
Tong Meng, Xuyang Jing, Zheng Yan, Witold Pedrycz
2019· article· en· Information Fusion· Computer Science
distilled prediction:candidate · noneconsensus · none
650
citations
affunlabeled
Deep Sets
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabás Póczos, Ruslan Salakhutdinov, Alexander J. Smola
2017· preprint· en· arXiv (Cornell University)· Computer Science
distilled prediction:candidate · noneconsensus · none
471
citations
afffundunlabeled
Outlier Detection
Azzedine Boukerche, Lining Zheng, Omar Alfandi
2020· review· en· ACM Computing Surveys· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
319
citations
affunlabeled
Deep Sets
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabás Póczos, Russ R. Salakhutdinov, Alexander J. Smola
2017· article· en· Neural Information Processing Systems· Computer Science
distilled prediction:candidate · scholarly_communicationconsensus · none
318
citations
affunlabeled
Ensembles for unsupervised outlier detection
Arthur Zimek, Ricardo J. G. B. Campello, Jörg Sander
2014· article· en· ACM SIGKDD Explorations Newsletter· Computer Science
distilled prediction:candidate · noneconsensus · none
273
citations
affunlabeled
K-Means-based isolation forest
Paweł Karczmarek, Adam Kiersztyn, Witold Pedrycz, Ebru Al
2020· article· en· Knowledge-Based Systems· Computer Science
distilled prediction:candidate · noneconsensus · none
172
citations
affunlabeled
Abnormal events detection based on spatio-temporal co-occurences
Yannick Benezeth, Pierre‐Marc Jodoin, Venkatesh Saligrama, Christophe Rosenberger
2009· article· en· 2009 IEEE Conference on Computer Vision and Pattern Recognition· Computer Science
distilled prediction:candidate · noneconsensus · none
150
citations
affunlabeled
Automated Analysis of Road Safety with Video Data
Nicolas Saunier, Tarek Sayed
2007· article· en· Transportation Research Record Journal of the Transportation Research Board· Computer Science
distilled prediction:candidate · noneconsensus · none
146
citations
affunlabeled
An introduction to deep learning
Francis Quintal Lauzon
2012· article· en· Computer Science
distilled prediction:candidate · noneconsensus · none
137
citations
affunlabeled
Machine learning approaches to network anomaly detection
Tarem Ahmed, Boris N. Oreshkin, Mark Coates
2007· article· en· USENIX workshop on Tackling computer systems problems with machine learning techniques· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
131
citations
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