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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 5 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.

aboutno affunlabeled
Detecting Malfunctions in Dynamic Systems
George E. P. Box, Spencer Graves, Søren Bisgaard, John Van Gilder, Ken Marko, J.R. James +3 more
2000· article· en· SAE technical papers on CD-ROM/SAE technical paper series· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
23
citations
affno abstractunlabeled
One-Shot Scene-Specific Crowd Counting.
Mohammad Asiful Hossain, K. M. Anil Kumar, Mehrdad Hosseinzadeh, Omit Chanda, Yang Wang
2019· article· en· British Machine Vision Conference· Computer Science
distilled prediction:candidate · noneconsensus · none
19
citations
afffundunlabeled
Deep Learning-Driven Anomaly Detection for Green IoT Edge Networks
Ahmad Shahnejat Bushehri, Ashkan Amirnia, Adel Belkhiri, Samira Keivanpour, Felipe Göhring de Magalhães, Gabriela Nicolescu
2023· article· en· IEEE Transactions on Green Communications and Networking· Computer Science
distilled prediction:candidate · stsconsensus · none
19
citations
affno abstractunlabeled
Bi-discriminator GAN for tabular data synthesis
Mohammad Esmaeilpour, Nourhene Chaalia, Adel Abusitta, Franşois-Xavier Devailly, Wissem Maazoun, Patrick Cardinal
2022· article· en· Pattern Recognition Letters· Computer Science
distilled prediction:candidate · noneconsensus · none
19
citations
affunlabeled
TrailSense
Keunseo Kim, Hengameh Zabihi, Heeyoung Kim, Uichin Lee
2017· article· en· Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies· Computer Science
distilled prediction:candidate · noneconsensus · none
17
citations
fundno affunlabeled
Model of Gradient Boosting Random Forest Prediction
Zhidong Zhang, Xiubin Zhu, Ding Liu
2022· article· en· 2022 IEEE International Conference on Networking, Sensing and Control (ICNSC)· Computer Science
distilled prediction:candidate · noneconsensus · none
17
citations
affno abstractunlabeled
Detecting the Onset of Machine Failure Using Anomaly Detection Methods
Mohammad Riazi, Osmar R. Zai͏̈ane, Tomoharu Takeuchi, Anthony Maltais, Johannes Günther, Micheal Lipsett
2019· book-chapter· en· Lecture notes in computer science· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
16
citations
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