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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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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.

57 results · 1 filter active ·
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20202025
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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.
57 works in the cohort · of 4,299,418page 1 of 2

Labels cover 0 of 57 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 57 of 57 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.

fundno affunlabeled
A scale-dependent measure of system dimensionality
Stefano Recanatesi, Serena Bradde, Vijay Balasubramanian, Nicholas A. Steinmetz, Eric Shea‐Brown
2022· article· en· Patterns· Neuroscience
machine prediction:candidate · noneconsensus · none
31
citations
afffundunlabeled
Thinking about Trust: People, Process, and Place
Stephen Marsh, Tosan Atele-Williams, Anirban Basu, Natasha Dwyer, Peter R. Lewis, Hector Miller-Bakewell +1 more
2020· review· en· Patterns· Social Sciences
machine prediction:candidate · noneconsensus · none
24
citations
afffundunlabeled
A consensus privacy metrics framework for synthetic data
Lisa Pilgram, Fida K. Dankar, Jörg Drechsler, Mark Elliot, Josep Domingo‐Ferrer, Paul Francis +9 more
2025· article· en· Patterns· Computer Science
machine prediction:candidate · noneconsensus · none
14
citations
affunlabeled
OpenML: Insights from 10 years and more than a thousand papers
Bernd Bischl, Giuseppe Casalicchio, Matthias Feurer, Sebastian Fischer, Pieter Gijsbers, S. P. Mukherjee +10 more
2025· article· en· Patterns· Computer Science
machine prediction:candidate · metaresearch+open_scienceconsensus · none
9
citations
affaboutunlabeled
The End-to-End Provenance Project
Aaron M. Ellison, Emery R. Boose, Barbara Lerner, Elizabeth Fong, Margo Seltzer
2020· article· en· Patterns· Decision Sciences
machine prediction:candidate · noneconsensus · none
6
citations
aboutno affunlabeled
The Uruguayan Digital Data Journey
Maria Laura Rodríguez Mendaro
2020· article· en· Patterns· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
5
citations
affunlabeled
The overfitted brain hypothesis
Luke Y. Prince, Blake A. Richards
2021· article· en· Patterns· Neuroscience
machine prediction:candidate · noneconsensus · none
3
citations

How this was built: Screen · Findings · About