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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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Visual Attention and Saliency Detection
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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.

509 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.
509 works in the cohort · of 4,299,418page 2 of 11

Labels cover 1 of 509 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 509 of 509 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
On metrics for measuring scanpath similarity
Ramin Fahimi, Neil D. B. Bruce
2020· review· en· Behavior Research Methods· Computer Science
machine prediction:candidate · noneconsensus · none
49
citations
afffundno abstractunlabeled
The Architecture of Object-Based Attention
Patrick Cavanagh, Gideon P. Caplovitz, Taissa K. Lytchenko, Marvin R. Maechler, Peter U. Tse, David L. Sheinberg
2023· review· en· Psychonomic Bulletin & Review· Computer Science
machine prediction:candidate · noneconsensus · none
49
citations
affunlabeled
Discrete Optimization for Shape Matching
Jing Ren, Simone Melzi, Peter Wonka, Maks Ovsjanikov
2021· article· en· Computer Graphics Forum· Computer Science
machine prediction:candidate · noneconsensus · none
48
citations
affunlabeled
PolyFit
Edoardo Alberto Dominici, Nico Schertler, Jonathan Griffin, Shayan Hoshyari, Leonid Sigal, Alla Sheffer
2020· article· en· ACM Transactions on Graphics· Computer Science
machine prediction:candidate · noneconsensus · none
35
citations
afffundno abstractunlabeled
Local contour symmetry facilitates scene categorization
John Wilder, Morteza Rezanejad, Sven Dickinson, Kaleem Siddiqi, Allan Jepson, Dirk B. Walther
2018· article· en· Cognition· Computer Science
machine prediction:candidate · noneconsensus · none
27
citations
affunlabeled
ATTENTION AND VISUAL SEARCH
Antonio Rodrı́guez-Sánchez, Evgueni Simine, John K. Tsotsos
2007· article· en· International Journal of Neural Systems· Computer Science
machine prediction:candidate · noneconsensus · none
27
citations
affno abstractunlabeled
Attention and Performance in Computational Vision
Glyn W. Humphreys, John K. Tsotsos, Erich Rome
2005· book· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
26
citations
affno abstractunlabeled
View combination in scene recognition
Alinda Friedman, David Waller
2008· article· en· Memory & Cognition· Computer Science
machine prediction:candidate · noneconsensus · none
26
citations

How this was built: Screen · Findings · About