MétaCan
Menu
Cohort builder

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.

Search term
Author
Year range
Sort
Language
Type
Field
Venue
Topic
Neuroscience and Music Perception
Retraction
Abstract
Evidence source
Study design
Label agreement
Label status

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.

2,941 results · 1 filter active ·
Results by year
20002025
Publication date
Categories
Machine labels · sparse coverage
Evidence
Language
Type
Citations
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 5 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
Neural Specializations for Tonal Processing
Robert J. Zatorre
2001· article· en· Annals of the New York Academy of Sciences· Neuroscience
machine prediction:candidate · noneconsensus · none
176
citations
affunlabeled
Perception and Production of Syncopated Rhythms
W. Tecumseh Fitch, Andrew Rosenfeld
2007· article· en· Music Perception An Interdisciplinary Journal· Neuroscience
machine prediction:candidate · noneconsensus · none
173
citations
affunlabeled
Music and the Brain
Robert J. Zatorre
2003· review· en· Annals of the New York Academy of Sciences· Neuroscience
machine prediction:candidate · noneconsensus · none
164
citations
affunlabeled
Maturation of fetal responses to music
B.S. Kisilevsky, Sylvia M. J. Hains, A Jacquet, Carolyn Granier‐Deferre, J.P. Lecanuet
2004· article· en· Developmental Science· Neuroscience
machine prediction:candidate · noneconsensus · none
163
citations
affunlabeled
The Song Is You
David M. Greenberg, Michał Kosiński, David Stillwell, Brian Monteiro, Daniel J. Levitin, Peter J. Rentfrow
2016· article· en· Social Psychological and Personality Science· Neuroscience
machine prediction:candidate · noneconsensus · none
158
citations
afffundunlabeled
Valproate reopens critical-period learning of absolute pitch
Judit Gervain, Bradley W. Vines, Rubo J. Seo, Takao K. Hensch, Janet F. Werker, Allan H. Young
2013· article· en· Frontiers in Systems Neuroscience· Neuroscience
machine prediction:candidate · noneconsensus · none
158
citations
affunlabeled
Music and Learning‐Induced Cortical Plasticity
Christo Pantev, Bernhard Roß, Takako Fujioka, Laurel J. Trainor, Michael Schulte, Matthias Schulz
2003· review· en· Annals of the New York Academy of Sciences· Neuroscience
machine prediction:candidate · noneconsensus · none
156
citations
affunlabeled
Cross-Cultural Work in Music Cognition
Nori Jacoby, Elizabeth Hellmuth Margulis, Martin Clayton, Erin E. Hannon, Henkjan Honing, John R. Iversen +14 more
2020· article· en· Music Perception An Interdisciplinary Journal· Neuroscience
machine prediction:candidate · noneconsensus · none
156
citations

How this was built: Screen · Findings · About