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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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Face and Expression Recognition
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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
fundfunder
venuejournal
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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.

895 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.
895 works in the cohort · of 4,299,418page 7 of 18

Labels cover 0 of 895 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 895 of 895 works in this cohort. Predictions are machine_predicted_unvalidated teacher distillation outputs. Candidate is the union; consensus is the intersection.

venueno affunlabeled
Fast spectral clustering with self-weighted features
Xiang Zhu, Zhiling Cai, Yu Ziniu, Junliang Wu, William Zhu
2022· article· en· Journal of Nonlinear and Variational Analysis· Computer Science
distilled prediction:candidate · noneconsensus · none
9
citations
affno abstractunlabeled
An Evaluation of Fuzzy Measure for Face Recognition
Paweł Karczmarek, Adam Kiersztyn, Witold Pedrycz
2017· book-chapter· en· Lecture notes in computer science· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
9
citations
affunlabeled
Face Recognition in Video Using a What-and-Where Fusion Neural Network
Michael P. Barry, Éric Granger
2007· article· en· IEEE International Conference on Neural Networks/IEEE ... International Conference on Neural Networks· Computer Science
distilled prediction:candidate · metaepi_narrow+scholarly_communication+research_integrityconsensus · none
8
citations
affunlabeled
A kernel machine based approach for multi-view face recognition
Juwei Lu, Konstantinos N. Plataniotis, A.N. Venetsanopoulos
2003· article· en· Proceedings - International Conference on Image Processing· Computer Science
distilled prediction:candidate · metaepi_narrow+scholarly_communicationconsensus · none
8
citations
afffundunlabeled
Pruning variable selection ensembles
Chunxia Zhang, Yilei Wu, Mu Zhu
2019· article· en· Statistical Analysis and Data Mining The ASA Data Science Journal· Computer Science
distilled prediction:candidate · scholarly_communicationconsensus · none
8
citations
fundno affno abstractunlabeled
Timid semi–supervised learning for face expression analysis
Mihai Badea, Corneliu Florea, Andrei Racoviţeanu, Laura Florea, Constantin Vertan
2023· article· en· Pattern Recognition· Computer Science
distilled prediction:candidate · insufficient_payloadconsensus · none
8
citations
afffundunlabeled
K-Nearest Oracle for Dynamic Ensemble Selection
Albert H.R. Ko, Robert Sabourin, Alceu de Souza Britto
2007· article· en· Proceedings of the International Conference on Document Analysis and Recognition· Computer Science
distilled prediction:candidate · noneconsensus · none
7
citations
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