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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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Neuroscience and Music Perception
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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.

2,941 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.
2,941 works in the cohort · of 4,299,418page 32 of 59

Labels cover 3 of 2,941 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 2,941 of 2,941 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
Lessons from the laboratory
Michael Schutz
2016· book-chapter· en· Cambridge University Press eBooks· Neuroscience
machine prediction:candidate · noneconsensus · none
13
citations
affunlabeled
Passion, music, and psychological well-being
Merrick Powell, Kirk N. Olsen, Robert J. Vallerand, William Forde Thompson
2023· article· en· Musicae Scientiae· Neuroscience
machine prediction:candidate · noneconsensus · none
13
citations
afffundunlabeled
What Makes Musical Prodigies?
Chanel Marion-St-Onge, Michael W. Weiss, Megha Sharda, Isabelle Peretz
2020· article· en· Frontiers in Psychology· Neuroscience
machine prediction:candidate · noneconsensus · none
13
citations
affno abstractunlabeled
Discriminating the tempo variations of a musical excerpt
Simon Grondin, Martin Laforest
2004· article· en· Nippon Onkyo Gakkaishi/Acoustical science and technology/Nihon Onkyo Gakkaishi· Neuroscience
machine prediction:candidate · noneconsensus · none
13
citations
afffundunlabeled
Memory disorders and vocal performance
Simone Dalla Bella, Alexandra Tremblay‐Champoux, Magdalena Berkowska, Isabelle Peretz
2012· review· en· Annals of the New York Academy of Sciences· Neuroscience
machine prediction:candidate · noneconsensus · none
12
citations
affunlabeled
Cross-modal melodic contour similarity
Jon B. Prince, Mark A. Schmuckler, William Forde Thompson
2009· article· en· Murdoch Research Repository (Murdoch University)· Neuroscience
machine prediction:candidate · noneconsensus · none
12
citations
affno abstractunlabeled
Objective Measures of Auditory Scene Analysis
Robert P. Carlyon, Sarah K. Thompson, Antje Heinrich, Friedemann Pulvermüller, Matthew H. Davis, Yury Shtyrov +2 more
2010· book-chapter· en· Neuroscience
machine prediction:candidate · noneconsensus · none
12
citations

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