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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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IEEE Transactions on Visualization and Computer Graphics
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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
fundfunder
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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.

288 results · 1 filter active ·
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20042025
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Machine labels · sparse coverage
Evidence
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
288 works in the cohort · of 4,299,418page 2 of 6

Labels cover 1 of 288 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 288 of 288 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.

affunlabeled
Visual Thinking In Action: Visualizations As Used On Whiteboards
Jagoda Walny, Sheelagh Carpendale, Nathalie Henry Riche, Gina Venolia, Phil Fawcett
2011· article· en· IEEE Transactions on Visualization and Computer Graphics· Computer Science
machine prediction:candidate · noneconsensus · none
92
citations
afffundunlabeled
Deep 6-DOF Tracking
Mathieu Garon, Jean‐François Lalonde
2017· article· en· IEEE Transactions on Visualization and Computer Graphics· Computer Science
machine prediction:candidate · noneconsensus · none
90
citations
affunlabeled
Exploratory Analysis of Time-Series with ChronoLenses
Jian Zhao, Fanny Chevalier, Emmanuel Pietriga, Ravin Balakrishnan
2011· article· en· IEEE Transactions on Visualization and Computer Graphics· Computer Science
machine prediction:candidate · noneconsensus · none
90
citations
affunlabeled
Illusion of Causality in Visualized Data
Cindy Xiong, Joel Shapiro, Jessica Hullman, Steven Franconeri
2019· article· en· IEEE Transactions on Visualization and Computer Graphics· Computer Science
machine prediction:candidate · noneconsensus · none
71
citations
aboutno affunlabeled
Evaluating ‘Graphical Perception’ with CNNs
Daniel Haehn, James Tompkin, Hanspeter Pfister
2018· article· en· IEEE Transactions on Visualization and Computer Graphics· Computer Science
machine prediction:candidate · noneconsensus · none
68
citations
affunlabeled
Artifacts caused by simplicial subdivision
Hamish Carr, Torsten Möller, Jack Snoeyink
2006· article· en· IEEE Transactions on Visualization and Computer Graphics· Engineering
machine prediction:candidate · noneconsensus · none
66
citations
affunlabeled
eSeeTrack—Visualizing Sequential Fixation Patterns
Hoi Ying Tsang, Melanie Tory, Charles Swindells
2010· article· en· IEEE Transactions on Visualization and Computer Graphics· Computer Science
machine prediction:candidate · noneconsensus · none
65
citations
afffundunlabeled
Spatialization Design: Comparing Points and Landscapes
Melanie Tory, David Sprague, Fuqu Wu, Wing Yan So, Tamara Munzner
2007· article· en· IEEE Transactions on Visualization and Computer Graphics· Computer Science
machine prediction:candidate · noneconsensus · none
62
citations
affunlabeled
ABySS-Explorer: Visualizing Genome Sequence Assemblies
Cydney Nielsen, Shaun D. Jackman, İnanç Birol, Steven J.M. Jones
2009· article· en· IEEE Transactions on Visualization and Computer Graphics· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
61
citations
affunlabeled
Enhancing Depth Perception in Translucent Volumes
Marta Kersten‐Oertel, James Stewart, Nikolaus F. Troje, Randy E. Ellis
2006· article· en· IEEE Transactions on Visualization and Computer Graphics· Computer Science
machine prediction:candidate · noneconsensus · none
56
citations
afffundunlabeled
Visual Signatures in Video Visualization
Min Chen, Ralf P. Botchen, Daniel Weiskopf, Thomas Ertl, Ian M. Thornton
2006· article· en· IEEE Transactions on Visualization and Computer Graphics· Computer Science
machine prediction:candidate · noneconsensus · none
54
citations
affunlabeled
Action-Based Multifield Video Visualization
Ralf P. Botchen, Sven Bachthaler, Fabian Schick, Min Chen, Greg Mori, Daniel Weiskopf +1 more
2008· article· en· IEEE Transactions on Visualization and Computer Graphics· Computer Science
machine prediction:candidate · noneconsensus · none
54
citations
afffundunlabeled
Aura 3D Textures
Xuejie Qin, Yee‐Hong Yang
2007· article· en· IEEE Transactions on Visualization and Computer Graphics· Computer Science
machine prediction:candidate · noneconsensus · none
51
citations
afffundunlabeled
Visualizing Causal Semantics Using Animations
Nivedita R. Kadaba, Pourang Irani, Jason P. Leboe
2007· article· en· IEEE Transactions on Visualization and Computer Graphics· Psychology
machine prediction:candidate · noneconsensus · none
45
citations
affunlabeled
Hue-Preserving Color Blending
Johnson Chuang, Daniel Weiskopf, Torsten Möller
2009· article· en· IEEE Transactions on Visualization and Computer Graphics· Computer Science
machine prediction:candidate · noneconsensus · none
43
citations

How this was built: Screen · Findings · About