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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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Neural dynamics and brain function
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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.

affaffiliation
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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.

4,098 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.
4,098 works in the cohort · of 4,299,418page 64 of 82

Labels cover 0 of 4,098 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 4,098 of 4,098 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
Inter-regional delays fluctuate in the human cerebral cortex
Joon-Young Moon, Kathrin Müsch, Charles E. Schroeder, Taufik A. Valiante, Christopher J. Honey
2022· preprint· en· bioRxiv (Cold Spring Harbor Laboratory)· Neuroscience
machine prediction:candidate · noneconsensus · none
1
citations
fundno affunlabeled
Combating fuzziness with computational modeling
Lucia M. Talamini, Martijn Meeter, Jaap M. J. Murre
2003· article· en· Behavioral and Brain Sciences· Neuroscience
machine prediction:candidate · noneconsensus · none
1
citations
afffundunlabeled
Pynapple, a toolbox for data analysis in neuroscience
Guillaume Viejo, Daniel Levenstein, Sofía Skromne Carrasco, Dhruv Mehrotra, Sara Mahallati, Gilberto R Vite +4 more
2023· article· en· eLife· Neuroscience
machine prediction:candidate · noneconsensus · none
1
citations
afffundunlabeled
Burst coding despite unimodal interval distributions
Ezekiel Williams, Alexandre Payeur, Albert Gidon, Richard Naud
2021· preprint· en· bioRxiv (Cold Spring Harbor Laboratory)· Neuroscience
machine prediction:candidate · noneconsensus · none
1
citations
fundno affunlabeled
How Visual is Visual Prediction?
Lauren L. Emberson, Ashley Rizzieri, Richard Ν. Aslin
2017· article· en· Infancy· Neuroscience
machine prediction:candidate · noneconsensus · none
1
citations

How this was built: Screen · Findings · About