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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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Gaze Tracking and Assistive Technology
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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.

688 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.
688 works in the cohort · of 4,299,418page 4 of 14

Labels cover 0 of 688 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 688 of 688 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.

affno abstractunlabeled
Eye Gaze in Intelligent User Interfaces
Yukiko Nakano, Cristina Conati, Thomas K. Bader
2013· book· en· Computer Science
machine prediction:candidate · noneconsensus · none
23
citations
affunlabeled
Allocating visual attention to grouped objects
Michael D. Dodd, Jay Pratt
2004· article· en· The European Journal of Cognitive Psychology· Computer Science
machine prediction:candidate · noneconsensus · none
20
citations
afffundunlabeled
Look to Go
Yuan Yuan Qian, Robert J. Teather
2018· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
19
citations
affunlabeled
Gaze as an Indicator of Input Recognition Errors
Candace E. Peacock, Ben Lafreniere, Ting Zhang, Stephanie Santosa, Hrvoje Benko, Tanya R. Jonker
2022· article· en· Proceedings of the ACM on Human-Computer Interaction· Computer Science
machine prediction:candidate · noneconsensus · none
19
citations
afffundunlabeled
Designing a gaze gesture guiding system
William Delamare, Teng Han, Pourang Irani
2017· preprint· en· Computer Science
machine prediction:candidate · noneconsensus · none
19
citations
affunlabeled
Path Word
Almoctar Hassoumi, Pourang Irani, Vsevolod Peysakhovich, Christophe Hurter
2018· preprint· en· Computer Science
machine prediction:candidate · noneconsensus · none
19
citations
affno abstractunlabeled
Eye-head gaze shifts
Brian D. Corneil
2011· book· en· Oxford University Press eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
17
citations
affunlabeled
eyeView
Tracy A. Jenkin, Jesse McGeachie, David Fono, Roel Vertegaal
2005· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
17
citations
afffundunlabeled
A kinematic model for 3-D head-free gaze-shifts
Mehdi Daemi, J. Douglas Crawford
2015· article· en· Frontiers in Computational Neuroscience· Computer Science
machine prediction:candidate · noneconsensus · none
16
citations

How this was built: Screen · Findings · About