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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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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 21 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. 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.

affunlabeled
Detecting Fake Points of Interest from Location Data
Syed Raza Bashir, Vojislav B. Mišić
2021· preprint· en· 2021 IEEE International Conference on Big Data (Big Data)· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Anomaly Detection with SDAE
Benjamin C. Smith, Kevin Cant, Gloria Wang
2020· preprint· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Collision Frequency
Keith J. Laidler
2016· dataset· en· IUPAC Standards Online· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Law Enforcement Companion
Manjula A. K, M Nirmila, Sourabh Navaratna, Shreyas Chaudhary, Deepesh Kumar
2022· article· en· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
venueno affunlabeled
Will Rogers Is Jenksing Police Response Times
Simon Demers
2017· article· en· Canadian Journal of Criminology and Criminal Justice/La Revue canadienne de criminologie et de justice pénale· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Anomaly detection in multi-class time series
Weihong Wang, Zhuolin Wu, Xuan Liu, Lei Jia, Xiaoguang Wang
2021· article· en· Journal of Physics Conference Series· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Language Models for Novelty Detection in Kernel Traces
Quentin Fournier, Daniel Aloise, Leandro R. Costa
2023· article· en· Zenodo (CERN European Organization for Nuclear Research)· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
An Axiomatic Perspective on Anomaly Detection
Chester Wyke, Ruth Urner
2024· book-chapter· en· Frontiers in artificial intelligence and applications· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Frustration: a generic mechanism to improve autonomy in robotics.
Adrien Jauffret, Marwen Belkaid, Nicolas Cuperlier, Philippe Gaussier, Philippe Tarroux
2013· preprint· en· HAL (Le Centre pour la Communication Scientifique Directe)· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affno abstractunlabeled
Nanaimo Free Press [Saturday, August 29, 1896]
2019· other· en· VIURRSpace (Vancouver Island University)· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affunlabeled
Rough K-means Outlier Factor Based on Entropy Computation
Djoko Budiyanto Setyohadi, Azuraliza Abu Bakar, Zulaiha Ali Othman
2014· article· en· Research Journal of Applied Sciences Engineering and Technology· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Introduction to the Minitrack on Digital Transformations of Business Operations
Jie Zhang, Yabing Jiang, Abraham Seidmann
2023· article· en· Proceedings of the ... Annual Hawaii International Conference on System Sciences/Proceedings of the Annual Hawaii International Conference on System Sciences· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About