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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
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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 8 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.

afffundunlabeled
Optimal Sparse Regression Trees
Rui Zhang, Rui Xin, Margo Seltzer, Cynthia Rudin
2023· article· en· Proceedings of the AAAI Conference on Artificial Intelligence· Computer Science
distilled prediction:candidate · insufficient_payloadconsensus · none
7
citations
affno abstractunlabeled
Curvelet Entropy for Facial Expression Recognition
Ashirbani Saha, Q. M. Jonathan Wu
2010· book-chapter· en· Lecture notes in computer science· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
7
citations
afffundunlabeled
Deep Multirepresentation Learning for Data Clustering
Mohammadreza Sadeghi, Narges Armanfard
2023· article· en· IEEE Transactions on Neural Networks and Learning Systems· Computer Science
distilled prediction:candidate · noneconsensus · none
7
citations
afffundunlabeled
Help-training for semi-supervised discriminative classifiers. Application to SVM
Mathias M. Adankon, Mohamed Cheriet
2008· article· en· Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition· Computer Science
distilled prediction:candidate · metaepi_narrow+scholarly_communication+insufficient_payloadconsensus · metaepi_narrow+insufficient_payload
6
citations
affno abstractunlabeled
Perfect histogram matching PCA for face recognition
Ana-Maria Sevcenco, Wu-Sheng Lu
2010· article· en· Multidimensional Systems and Signal Processing· Computer Science
distilled prediction:candidate · noneconsensus · none
6
citations
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How this was built: Screen · Findings · About