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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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Functional Brain Connectivity Studies
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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.

5,853 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.
5,853 works in the cohort · of 4,299,418page 2 of 118

Labels cover 13 of 5,853 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 5,853 of 5,853 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
Brain templates and atlases
Alan C. Evans, Andrew L. Janke, D. Louis Collins, Sylvain Baillet
2012· review· en· NeuroImage· Neuroscience
machine prediction:candidate · noneconsensus · none
712
citations
affunlabeled
Deep learning for neuroimaging: a validation study
Sergey Plis, Devon Hjelm, Ruslan Salakhutdinov, Elena A. Allen, H. Jeremy Bockholt, Jeffrey D. Long +4 more
2014· article· en· Frontiers in Neuroscience· Neuroscience
machine prediction:candidate · noneconsensus · none
617
citations
affunlabeled
Modeling the Impact of Lesions in the Human Brain
Jeffrey Alstott, Michael Breakspear, Patric Hagmann, Leila Cammoun, Olaf Sporns
2009· article· en· PLoS Computational Biology· Neuroscience
machine prediction:candidate · noneconsensus · none
586
citations
afffundunlabeled
Exercise, brain, and cognition across the life span
Michelle W. Voss, Lindsay S. Nagamatsu, Teresa Liu‐Ambrose, Arthur F. Kramer
2011· review· en· Journal of Applied Physiology· Neuroscience
machine prediction:candidate · noneconsensus · none
582
citations
affunlabeled
Gradients of structure–function tethering across neocortex
Bertha Vázquez-Rodríguez, Laura E. Suárez, Ross D. Markello, Golia Shafiei, Casey Paquola, Patric Hagmann +4 more
2019· article· en· Proceedings of the National Academy of Sciences· Neuroscience
machine prediction:candidate · noneconsensus · none
572
citations
afffundunlabeled
Atypical functional connectome hierarchy in autism
Seok‐Jun Hong, Reinder Vos de Wael, Richard A. I. Bethlehem, Sara Larivière, Casey Paquola, Sofie L. Valk +5 more
2019· article· en· Nature Communications· Neuroscience
machine prediction:candidate · noneconsensus · none
567
citations
affunlabeled
Guidelines for reporting an fMRI study
Russell A. Poldrack, Paul C. Fletcher, Richard N. Henson, Keith J. Worsley, Matthew Brett, Thomas E. Nichols
2008· article· en· NeuroImage· Neuroscience
machine prediction:candidate · metaresearchconsensus · metaresearch
563
citations
afffundunlabeled
NeuroMark: An automated and adaptive ICA based pipeline to identify reproducible fMRI markers of brain disorders
Yuhui Du, Zening Fu, Jing Sui (Beijing Normal University), my correct affiliation is beijing normal university, not Qingdao University of Science and Technology, please correct the current affiliation. Thank you, Shuang Gao, Ying Xing, Dongdong Lin +8 more
2020· article· en· NeuroImage Clinical· Neuroscience
machine prediction:candidate · noneconsensus · none
463
citations

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