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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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Robotics and Automated Systems
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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
venuejournal
aboutaboutness

The four routes compose: require the funder route and exclude affiliation to get the funder-only stratum no affiliation-based frame ever sees.

295 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.
295 works in the cohort · of 4,299,418page 3 of 6

Labels cover 0 of 295 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 295 of 295 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
10081 Abstracts Collection – Cognitive Robotics
Gerhard Lakemeyer, Hector J. Levesque, Fiora Pirri
2010· article· en· DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)· Engineering
machine prediction:candidate · insufficient_payloadconsensus · none
1
citations
aboutno affunlabeled
The ViewRay™ System
Daniel A. Low, RICHARD B. STARK, James F. Dempsey
2011· book-chapter· en· Engineering
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Optimizing data links in the TN-NTN world
Indraditya Bhattacharyya, William A. Powell, Steven Petten
2024· article· en· IET conference proceedings.· Engineering
machine prediction:candidate · noneconsensus · none
1
citations
venueno affunlabeled
Adaptable ROBO-intelligences.
Dumitru Todoroi
2017· article· en· Journal of the American Romanian Academy of Arts and Sciences· Engineering
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Path tracking robot based on A* and Dijkstra
Zixuan Chen, Hao Jun Li, Yao Liu
2024· article· en· AIP conference proceedings· Engineering
machine prediction:candidate · noneconsensus · none
1
citations
fundno affunlabeled
TMT approach to observatory software development process
Hanne Buur, Annapurni Subramaniam, Kim Gillies, Christophe Dumas, Ravinder Bhatia
2016· article· en· Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE· Engineering
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Collaboration and Technology
Julita Vassileva, Ulrich Hoppe, Hiroaki Ogata, Takaya Yuizono
2016· book· en· Lecture notes in computer science· Engineering
machine prediction:candidate · noneconsensus · none
1
citations
fundno affunlabeled
Implementing KaRo
Tomasz M. Wolniewicz
2004· book-chapter· en· Engineering
machine prediction:candidate · noneconsensus · none
1
citations

How this was built: Screen · Findings · About