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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.

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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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Advanced Neuroimaging Techniques and Applications
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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.

2,751 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.
2,751 works in the cohort · of 4,299,418page 1 of 56

Labels cover 6 of 2,751 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 2,751 of 2,751 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.

afffundunlabeled
Dipy, a library for the analysis of diffusion MRI data
Eleftherios Garyfallidis, Matthew Brett, Bagrat Amirbekian, Ariel Rokem, Stéfan van der Walt, Maxime Descoteaux +1 more
2014· article· en· Frontiers in Neuroinformatics· Medicine
machine prediction:candidate · noneconsensus · none
1,462
citations
afffundunlabeled
Diffusion MRI fiber tractography of the brain
Ben Jeurissen, Maxime Descoteaux, Susumu Mori, Alexander Leemans
2017· review· en· NMR in Biomedicine· Medicine
machine prediction:candidate · noneconsensus · none
600
citations
affunlabeled
Training of Working Memory Impacts Structural Connectivity
Hikaru Takeuchi, Atsushi Sekiguchi, Yasuyuki Taki, Satoru Yokoyama, Yukihito Yomogida, Nozomi Komuro +3 more
2010· article· en· Journal of Neuroscience· Medicine
machine prediction:candidate · noneconsensus · none
536
citations
affno abstractunlabeled
Networks of anatomical covariance
Alan C. Evans
2013· review· en· NeuroImage· Medicine
machine prediction:candidate · noneconsensus · none
434
citations
affno abstractunlabeled
Insights into brain microstructure from the T2 distribution
Alex L. MacKay, Cornelia Laule, Irene M. Vavasour, Thorarin A. Bjarnason, Shannon Kolind, Burkhard Mädler
2006· article· en· Magnetic Resonance Imaging· Medicine
machine prediction:candidate · noneconsensus · none
356
citations
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How this was built: Screen · Findings · About