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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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Memory and Neural Mechanisms
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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.

2,640 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.
2,640 works in the cohort · of 4,299,418page 39 of 53

Labels cover 4 of 2,640 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 2,640 of 2,640 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
Place versus response learning in rats
Mark R. Cole, Amy Clipperton, Caryn Walt
2007· article· en· Learning & Behavior· Neuroscience
machine prediction:candidate · noneconsensus · none
4
citations
affno abstractunlabeled
Multiple Memory Systems
Norman M. White
2009· book-chapter· en· Elsevier eBooks· Neuroscience
machine prediction:candidate · noneconsensus · none
4
citations
affunlabeled
NeuralBasis of Human Fear Learning
Joseph E. Dunsmoor, Kevin S. LaBar
2013· book-chapter· en· Cambridge University Press eBooks· Neuroscience
machine prediction:candidate · noneconsensus · none
3
citations
afffundno abstractunlabeled
Aging and Cognition☆
Patrick S. R. Davidson, Gordon Winocur
2016· book-chapter· en· Elsevier eBooks· Neuroscience
machine prediction:candidate · noneconsensus · none
3
citations
affunlabeled
The Hippocampus and Episodic Memory in Children
Mary Lou Smith, Mary Pat McAndrews
2013· article· en· Journal of the International Neuropsychological Society· Neuroscience
machine prediction:candidate · noneconsensus · none
3
citations
afffundunlabeled
Transplanting brains?
Nils‐Frederic Wagner
2016· article· en· South African Journal of Philosophy· Neuroscience
machine prediction:candidate · noneconsensus · none
3
citations
affunlabeled
Predictive Coding of Reward in the Hippocampus
Mohammad Yaghoubi, Andrés Nieto‐Posadas, Coralie‐Anne Mosser, Thomas Gisiger, Émmanuel Wilson, Sylvain Williams +1 more
2024· preprint· en· bioRxiv (Cold Spring Harbor Laboratory)· Neuroscience
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
Rat spatial memory and foraging on dual radial mazes
William A. Roberts, Krista Macpherson, Sophia G. Robinson, Abagail Hennessy, Bram Richmond
2023· article· en· Learning & Behavior· Neuroscience
machine prediction:candidate · noneconsensus · none
3
citations
afffundunlabeled
Systems consolidation impairs behavioral flexibility
Sankirthana Sathiyakumar, Sofía Skromne Carrasco, Lydia Saad, Blake A. Richards
2020· article· en· Learning & Memory· Neuroscience
machine prediction:candidate · noneconsensus · none
3
citations

How this was built: Screen · Findings · About