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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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Neural and Behavioral Psychology 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.

3,183 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.
3,183 works in the cohort · of 4,299,418page 27 of 64

Labels cover 5 of 3,183 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 3,183 of 3,183 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.

afffundno abstractunlabeled
Tracing the emergence of the memorability benefit
Greer Gillies, Hyun Park, Jason C. S. Woo, Dirk B. Walther, Jonathan S. Cant, Keisuke Fukuda
2023· article· en· Cognition· Neuroscience
machine prediction:candidate · noneconsensus · none
22
citations
afffundunlabeled
The neural signature of the decision value of future pain
Michel‐Pierre Coll, Hocine Slimani, Choong‐Wan Woo, Tor D. Wager, Pierre Rainville, Étienne Vachon‐Presseau +1 more
2022· article· en· Proceedings of the National Academy of Sciences· Neuroscience
machine prediction:candidate · noneconsensus · none
22
citations
affno abstractunlabeled
On finding negative priming from distractors
John Christie, Raymond M. Klein
2008· article· en· Psychonomic Bulletin & Review· Neuroscience
machine prediction:candidate · noneconsensus · none
21
citations
affunlabeled
Attentional Bias for Exercise-Related Images
Tanya R. Berry, John C. Spence, Sean Stolp
2011· article· en· Research Quarterly for Exercise and Sport· Neuroscience
machine prediction:candidate · noneconsensus · none
21
citations
affunlabeled
Two Myths about Somatic Markers
Stefan Linquist, Jordan Bartol
2012· article· en· The British Journal for the Philosophy of Science· Neuroscience
machine prediction:candidate · noneconsensus · none
21
citations
afffundunlabeled
The effects of age and task demands on visual selective attention.
Paula McLaughlin, Carolyn Szostak, Malcolm A. Binns, Fergus I. M. Craik, Steven P. Tipper, Donald T. Stuss
2010· article· en· Canadian Journal of Experimental Psychology/Revue canadienne de psychologie expérimentale· Neuroscience
machine prediction:candidate · noneconsensus · none
20
citations
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