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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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Aesthetic Perception and Analysis
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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
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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.

429 results · 1 filter active ·
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20012025
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Machine labels · sparse coverage
Evidence
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
429 works in the cohort · of 4,299,418page 2 of 9

Labels cover 2 of 429 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 429 of 429 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
Visualizing and Analyzing the Mona Lisa
Louis Borgeat, Guy Godin, Philippe Massicotte, Guillaume Poirier, François Blais, J.‐A. Beraldin
2007· review· en· IEEE Computer Graphics and Applications· Neuroscience
machine prediction:candidate · noneconsensus · none
25
citations
fundno affunlabeled
Exploring Co-creative Drawing Workflows
Chipp Jansen, Elizabeth Sklar
2021· article· en· Frontiers in Robotics and AI· Neuroscience
machine prediction:candidate · noneconsensus · none
21
citations
affunlabeled
Aesthetic Experts, Guides to Value
Dominic McIver Lopes
2015· article· en· Journal of Aesthetics and Art Criticism· Neuroscience
machine prediction:candidate · noneconsensus · none
19
citations
affunlabeled
Simply Spinning
Shannon Cuykendall, Ethan Soutar-Rau, Karen Anne Cochrane, Jacob Freiberg, Thecla Schiphorst
2015· article· en· Neuroscience
machine prediction:candidate · noneconsensus · none
16
citations
afffundunlabeled
Toward a Unification of the Arts
Steven Brown
2018· article· en· Frontiers in Psychology· Neuroscience
machine prediction:candidate · noneconsensus · none
15
citations
affunlabeled
Aesthetics and Cognitive Science
Dustin Stokes
2009· article· en· Philosophy Compass· Neuroscience
machine prediction:candidate · noneconsensus · none
15
citations
aboutno affunlabeled
How Do We Listen To Museums?
Kathleen Wiens, Éric de Visscher
2019· article· en· Curator The Museum Journal· Neuroscience
machine prediction:candidate · noneconsensus · none
13
citations
affunlabeled
The Perception and Evaluation of Visual Art
Henrik Hagtvedt, Vanessa M. Patrick, Reidar Hagtvedt
2008· article· en· Empirical Studies of the Arts· Neuroscience
machine prediction:candidate · noneconsensus · none
11
citations
affunlabeled
Art, authenticity and appropriation
James O. Young
2006· article· en· Frontiers of Philosophy in China· Neuroscience
machine prediction:candidate · noneconsensus · none
11
citations
affunlabeled
Aesthetic Properties of Everyday Objects
Christine Stich, Jens Eisermann, Bärbel Knaüper, Helmut Leder
2007· article· en· Perceptual and Motor Skills· Neuroscience
machine prediction:candidate · noneconsensus · none
11
citations
affunlabeled
The Unification of the Arts
Steven Brown
2021· book· en· Neuroscience
machine prediction:candidate · noneconsensus · none
11
citations

How this was built: Screen · Findings · About