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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.

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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 15 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.

affunlabeled
Creating physical visualizations with makervis
Saiganesh Swaminathan, Conglei Shi, Yvonne Jansen, Pierre Dragicevic, Lora Oehlberg, Jean‐Daniel Fekete
2014· preprint· en· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affunlabeled
Mapping the "How" of Collaborative Action
Christine T. Wolf, Julia Bullard, Stacy Wood, Amelia Acker, Drew Paine, Charlotte P. Lee
2019· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affno abstractunlabeled
The Concept of Visualization
Linda M. Phillips, Stephen P. Norris, John S. Macnab
2010· book-chapter· en· Models and modeling in science education· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affno abstractunlabeled
Learning in Cobweb Experiments
Cars Hommes, Joep Sonnemans, Jan Tuinstra, Henk van de Velden
2003· article· en· SSRN Electronic Journal· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
venueno affunlabeled
Nonlinearity, Multilinearity, Simultaneity
Florian Hadler, Daniel Irrgang
2021· article· en· Interactive Film and Media Journal· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affunlabeled
Powering Visualization With Deep Learning
Yingcai Wu, Siwei Fu, Jian Zhao, Chris Bryan
2021· article· en· IEEE Computer Graphics and Applications· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affunlabeled
The Meta-Dex Suite
Michael Huggett, Edie Rasmussen
2011· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affunlabeled
Visual Sensitivity of Dynamic Graphical Objects
Munira Jessa, Catherine M. Burns
2005· article· en· Proceedings of the Human Factors and Ergonomics Society Annual Meeting· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
splot - visual analytics for spatial statistics
Stefanie Lumnitz, Daniel Arribas‐Bel, Renan Xavier Cortes, James Gaboardi, Verena C. Griess, Wei Kang +3 more
2020· article· en· The Journal of Open Source Software· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affunlabeled
Graphical Presentation of Longitudinal Data
Howard Wainer, Ian Spence
2005· other· en· Encyclopedia of Statistics in Behavioral Science· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations

How this was built: Screen · Findings · About