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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 12 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
Speculative Execution for Guided Visual Analytics
Fabian Sperrle, Jürgen Bernard, Michael Sedlmair, Daniel A. Keim, Mennatallah El‐Assady
2018· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
8
citations
affno abstractunlabeled
Self-organized Middle-Out Abstraction
Sebastian von Mammen, Jan-Philipp Steghöfer, Jörg Denzinger, Christian Jacob
2011· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
7
citations
affunlabeled
User Task Adaptation in Multimedia Presentations.
Giuseppe Carenini, Cristina Conati, Enamul Hoque, Ben Steichen
2013· article· en· International Conference on User Modeling, Adaptation, and Personalization· Computer Science
machine prediction:candidate · noneconsensus · none
7
citations
affunlabeled
Exploring the Design of Patient-Generated Data Visualizations
Fateme Rajabiyazdi, Charles Périn, Lora Oehlberg, Sheelagh Carpendale
2020· preprint· en· HAL (Le Centre pour la Communication Scientifique Directe)· Computer Science
machine prediction:candidate · noneconsensus · none
7
citations
affno abstractunlabeled
Computational Modeling of Criminal Activity
Uwe Glässer, Mona Vajihollahi
2008· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
7
citations
afffundunlabeled
ACH Walkthrough
Jeff Wilson, Judith M. Brown, Robert Biddle
2014· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
7
citations
afffundunlabeled
Path Tracing in 2D, 3D, and Physicalized Networks
Michael J. McGuffin, Ryan Servera, Marie Forest
2023· article· en· IEEE Transactions on Visualization and Computer Graphics· Computer Science
machine prediction:candidate · noneconsensus · none
7
citations
affunlabeled
A search-set model of path tracing in graphs
Jessica Q. Dawson, Tamara Munzner, Joanna McGrenere
2014· article· en· Information Visualization· Computer Science
machine prediction:candidate · noneconsensus · none
7
citations
affunlabeled
DARLS
Loutfouz Zaman, Ashish Kalra, Wolfgang Stuerzlinger
2011· article· en· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
6
citations

How this was built: Screen · Findings · About