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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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Handwritten Text Recognition Techniques
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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.

605 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.
605 works in the cohort · of 4,299,418page 1 of 13

Labels cover 0 of 605 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 605 of 605 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.

affno abstractunlabeled
Recognition and retrieval of mathematical expressions
Richard Zanibbi, Dorothea Blostein
2011· article· en· International Journal on Document Analysis and Recognition (IJDAR)· Computer Science
machine prediction:candidate · noneconsensus · none
287
citations
affunlabeled
A Perspective Analysis of Handwritten Signature Technology
Moises Díaz, Miguel A. Ferrer, Donato Impedovo, Muhammad Imran Malik, Giuseppe Pirlo, Réjean Plamondon
2019· review· en· ACM Computing Surveys· Computer Science
machine prediction:candidate · noneconsensus · none
249
citations
affno abstractunlabeled
Optical music recognition: state-of-the-art and open issues
Ana Rebelo, Ichiro Fujinaga, Filipe Paszkiewicz, A. Marçal, Carlos Guedes, Jaime S. Cardoso
2012· article· en· International Journal of Multimedia Information Retrieval· Computer Science
machine prediction:candidate · noneconsensus · none
248
citations
afffundunlabeled
A Comparative Study of Staff Removal Algorithms
Christoph Dalitz, Michael Droettboom, B. Pranzas, Ichiro Fujinaga
2008· article· en· IEEE Transactions on Pattern Analysis and Machine Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
120
citations
affunlabeled
Hidden Markov Models
Horst Bunke, Terry Caelli
2001· book· en· Series in machine perception and artificial intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
79
citations
affno abstractunlabeled
Writer verification using texture-based features
Regiane Kowalek Hanusiak, Luiz S. Oliveira, Edson Justino, Robert Sabourin
2011· article· en· International Journal on Document Analysis and Recognition (IJDAR)· Computer Science
machine prediction:candidate · noneconsensus · none
64
citations
affunlabeled
The spatio-temporal dynamics of visual letter recognition
Daniel Fiset, Caroline Blais, Martin Arguin, K. Tadros, Catherine Éthier-Majcher, Daniel N. Bub +1 more
2008· article· en· Cognitive Neuropsychology· Computer Science
machine prediction:candidate · noneconsensus · none
61
citations

How this was built: Screen · Findings · About