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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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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.

1,448 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.
1,448 works in the cohort · of 4,299,418page 16 of 29

Labels cover 4 of 1,448 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 1,448 of 1,448 works in this cohort. Predictions are machine_predicted_unvalidated teacher distillation outputs. Candidate is the union; consensus is the intersection.

affunlabeled
Visualising the<i>Titanic</i>Disaster
Michael Friendly, Jürgen Symanzik, Ortac Onder
2019· article· en· Significance· Computer Science
distilled prediction:candidate · insufficient_payloadconsensus · none
3
citations
aboutno affunlabeled
5G Visualization: The METIS-II Project Approach
David Martín‐Sacristán, Carlos Herranz, José F. Monserrat, Andrzej Szczygieł, Nandish P. Kuruvatti, David García‐Roger +4 more
2018· article· en· Mobile Information Systems· Computer Science
distilled prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
Pathways
Barbara J. Hunnicutt, Martin Krzywinski
2015· article· en· Nature Methods· Computer Science
distilled prediction:candidate · noneconsensus · none
3
citations
affunlabeled
Investigating lay evaluations of models
Patrick Bodilly Kane, Stephen B. Broomell
2021· article· en· Thinking & Reasoning· Computer Science
distilled prediction:candidate · noneconsensus · none
3
citations
affunlabeled
SerpentTI
Alex Cheng, Mary Malit, Chuanxi Zhang, Nick Koudas
2014· article· en· Computer Science
distilled prediction:candidate · noneconsensus · none
3
citations
afffundno abstractunlabeled
Visual analytics of high-frequency lake monitoring data
Mark P. Wachowiak, April L. James, Renata Wachowiak-Smolíková, Dan Walters, Krystopher J. Chutko, James A. Rusak
2017· article· en· International Journal of Data Science and Analytics· Computer Science
distilled prediction:candidate · scholarly_communication+open_scienceconsensus · none
2
citations
affunlabeled
VET3D
Craig Anslow, Stuart Marshall, James Noble, Robert Biddle
2006· article· en· Computer Science
distilled prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
FractVis: Visualizing Microseismic Events
Ahmed E. Mostafa, Sheelagh Carpendale, Emílio Vital Brazil, David W. Eaton, Ehud Sharlin, Mário Costa Sousa
2013· book-chapter· en· Lecture notes in computer science· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
2
citations
affunlabeled
Introducing fairness in network visualization
Peter Eades, Seok-Hee Hong, Giuseppe Liotta, Fabrizio Montecchiani, Martin Nöllenburg, Tommaso Piselli +1 more
2024· article· en· Information Sciences· Computer Science
distilled prediction:candidate · scholarly_communicationconsensus · none
2
citations
affunlabeled
The concept formerly known as information (the panel)
Jenna Hartel, Karen Pollock, Rebecca Noone
2013· article· en· Proceedings of the American Society for Information Science and Technology· Computer Science
distilled prediction:candidate · stsconsensus · none
2
citations
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