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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.

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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 5 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. 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.

afffundunlabeled
Towards User-Adaptive Information Visualization
Cristina Conati, Giuseppe Carenini, Dereck Toker, Sébastien Lallé
2015· article· en· Proceedings of the AAAI Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
40
citations
affunlabeled
<i>"Point it, split it, peel it, view it"</i>
Nicole Sultanum, Sowmya Somanath, Ehud Sharlin, Mário Costa Sousa
2011· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
40
citations
affgpt · no categoryopus · no categorymodels agree
Doing Better Data Visualization
Eric Hehman, Sally Y Xie
2021· article· en· Advances in Methods and Practices in Psychological Science· Computer Science
machine prediction:candidate · metaresearchconsensus · none
39
citations
affunlabeled
A Model-Based Visualization Taxonomy
Melanie Tory, Torsten Möller
2002· article· en· Chemosphere· Computer Science
machine prediction:candidate · noneconsensus · none
36
citations
affunlabeled
Broadening Intellectual Diversity in Visualization Research Papers
Bongshin Lee, Katherine E. Isaacs, Danielle Albers Szafir, G. Elisabeta Marai, Çağatay Turkay, Melanie Tory +2 more
2019· article· en· IEEE Computer Graphics and Applications· Computer Science
machine prediction:candidate · metaresearchconsensus · metaresearch
35
citations
affunlabeled
In Color Perception, Size Matters
Maureen Stone
2012· article· en· IEEE Computer Graphics and Applications· Computer Science
machine prediction:candidate · noneconsensus · none
34
citations
affunlabeled
It’s no Riddle, Choose the Middle
Craig Bennell, Brent Snook, Paul Taylor, Shevaun Corey, Julia Keyton
2007· article· en· Criminal Justice and Behavior· Computer Science
machine prediction:candidate · noneconsensus · none
34
citations
affunlabeled
DataInk
Haijun Xia, Nathalie Henry Riche, Fanny Chevalier, Bruno De Araujo, Daniel Wigdor
2018· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
34
citations
affunlabeled
Science of Analytical Reasoning
William Ribarsky, Brian Fisher, William M. Pottenger
2009· article· en· Information Visualization· Computer Science
machine prediction:candidate · noneconsensus · none
33
citations
affunlabeled
Qualitative analysis of visualization
Melanie Tory, Sheryl Staub‐French
2008· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
33
citations
affunlabeled
Label-and-Learn
Yunjia Sun, Edward Lank, Michael Terry
2017· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
32
citations
affunlabeled
Formalizing Emphasis in Information Visualization
Kyle Wm. Hall, Charles Périn, Peter G. Kusalik, Carl Gutwin, Sheelagh Carpendale
2016· article· en· Computer Graphics Forum· Computer Science
machine prediction:candidate · noneconsensus · none
31
citations
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