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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 3 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 overlap in processing music and speech
Isabelle Peretz, Dominique T. Vuvan, Marie-Élaine Lagrois, Jorge L. Armony
2015· review· en· Philosophical Transactions of the Royal Society B Biological Sciences· Neuroscience
machine prediction:candidate · noneconsensus · none
281
citations
affno abstractunlabeled
Songs as an aid for language acquisition
Daniele Schön, Maud Boyer, Sylvain Moreno, Mireille Besson, Isabelle Peretz, Régine Kolinsky
2007· article· en· Cognition· Neuroscience
machine prediction:candidate · noneconsensus · none
262
citations
afffundunlabeled
Neural correlates of specific musical anhedonia
Noelia Martínez‐Molina, Ernest Mas‐Herrero, Antoni Rodríguez‐Fornells, Robert J. Zatorre, Josep Marco‐Pallarés
2016· article· en· Proceedings of the National Academy of Sciences· Neuroscience
machine prediction:candidate · noneconsensus · none
232
citations
affunlabeled
Good Pitch Memory Is Widespread
E. Glenn Schellenberg, Sandra E. Trehub
2003· article· en· Psychological Science· Neuroscience
machine prediction:candidate · noneconsensus · none
225
citations
affunlabeled
Cross-cultural perspectives on music and musicality
Sandra E. Trehub, Judith Becker, Iain Morley
2015· review· en· Philosophical Transactions of the Royal Society B Biological Sciences· Neuroscience
machine prediction:candidate · noneconsensus · none
223
citations
affunlabeled
Volume of Left Heschl's Gyrus and Linguistic Pitch Learning
Patrick C. M. Wong, Catherine M. Warrier, Virginia B. Penhune, Ajit Kumar Roy, A. Sadehh, Todd B. Parrish +1 more
2007· article· en· Cerebral Cortex· Neuroscience
machine prediction:candidate · noneconsensus · none
222
citations
affunlabeled
The Psychology of Music: Rhythm and Movement
Daniel J. Levitin, Jessica A. Grahn, Justin London
2017· review· en· Annual Review of Psychology· Neuroscience
machine prediction:candidate · noneconsensus · none
222
citations

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