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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.

867 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.
867 works in the cohort · of 4,299,418page 3 of 18

Labels cover 10 of 867 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 867 of 867 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
Data Profiling
Ziawasch Abedjan, Lukasz Golab, Felix Naumann, Thorsten Papenbrock
2019· book· en· Synthesis lectures on data management· Decision Sciences
machine prediction:candidate · noneconsensus · none
31
citations
affno abstractunlabeled
Identity Management Architecture
Uwe Glässer, Mona Vajihollahi
2009· book-chapter· en· Annals of information systems· Decision Sciences
machine prediction:candidate · noneconsensus · none
30
citations
fundno affunlabeled
Issues in big data testing and benchmarking
A. Alexandrov, Christoph Brücke, Volker Markl
2013· article· en· Decision Sciences
machine prediction:candidate · metaresearchconsensus · none
29
citations
affunlabeled
Approximate joins: concepts and techniques
Nick Koudas, Divesh Srivastava
2005· article· en· Very Large Data Bases· Decision Sciences
machine prediction:candidate · noneconsensus · none
29
citations
fundno affunlabeled
Using clustering strategies for creating authority files
James C. French, Allison L. Powell, Eric Schulman
2000· article· en· Journal of the American Society for Information Science· Decision Sciences
machine prediction:candidate · noneconsensus · none
29
citations
affunlabeled
Astrid
Suraj Shetiya, Saravanan Thirumuruganathan, Nick Koudas, Gautam Das
2020· article· en· Proceedings of the VLDB Endowment· Decision Sciences
machine prediction:candidate · noneconsensus · none
29
citations
affunlabeled
Linkage Query Writer
Oktie Hassanzadeh, Reynold Xin, Renée J. Miller, Anastasios Kementsietsidis, Lipyeow Lim, Min Wang
2009· article· en· Proceedings of the VLDB Endowment· Decision Sciences
machine prediction:candidate · noneconsensus · none
25
citations
affunlabeled
A Formal Framework for Probabilistic Unclean Databases.
Christopher De, Ihab F. Ilyas, Benny Kimelfeld, Christopher Ré, Theodoros Rekatsinas
2019· article· en· DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)· Decision Sciences
machine prediction:candidate · noneconsensus · none
22
citations
affunlabeled
A demonstration of KGLac
Ahmed Helal, Mossad Helali, Khaled Ammar, Essam Mansour
2021· article· en· Proceedings of the VLDB Endowment· Decision Sciences
machine prediction:candidate · noneconsensus · none
21
citations
afffundaboutunlabeled
Unlocking First Nations health information through data linkage
Jennifer Walker, Evelyn Pyper, Carmen Jones, Saba Khan, Nelson W. Chong, Dan Legge +2 more
2018· article· en· International Journal for Population Data Science· Decision Sciences
machine prediction:candidate · noneconsensus · none
21
citations
affunlabeled
Data Profiling
Ziawasch Abedjan, Lukasz Golab, Felix Naumann
2018· book· en· Decision Sciences
machine prediction:candidate · noneconsensus · none
20
citations

How this was built: Screen · Findings · About