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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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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.

125 results · 1 filter active ·
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20032025
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Machine labels · sparse coverage
Evidence
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
125 works in the cohort · of 4,299,418page 1 of 3

Labels cover 0 of 125 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 125 of 125 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
A signal detection analysis of contingency data
Lorraine G. Allan, Shepard Siegel, Jason M. Tangen
2005· article· en· Learning & Behavior· Decision Sciences
machine prediction:candidate · noneconsensus · none
78
citations
affno abstractunlabeled
An instance theory of associative learning
Randall K. Jamieson, Matthew J. C. Crump, Samuel D. Hannah
2011· article· en· Learning & Behavior· Neuroscience
machine prediction:candidate · noneconsensus · none
71
citations
affno abstractunlabeled
Learning and the wisdom of the body
Steven J. Siegel
2008· article· en· Learning & Behavior· Psychology
machine prediction:candidate · noneconsensus · none
37
citations
affno abstractunlabeled
Introduction
Bennett G. Galef, Cecilia Heyes
2004· article· en· Learning & Behavior· Psychology
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
24
citations
afffundno abstractunlabeled
Dynamic object recognition in pigeons and humans
Marcia L. Spetch, Alinda Friedman, Quoc C. Vuong
2006· article· en· Learning & Behavior· Psychology
machine prediction:candidate · noneconsensus · none
21
citations
afffundno abstractunlabeled
Predicting shifts in generalization gradients with perceptrons
Matthew G. Wisniewski, Milen L. Radell, Lauren M. Guillette, Christopher B. Sturdy, Eduardo Mercado
2011· article· en· Learning & Behavior· Computer Science
machine prediction:candidate · noneconsensus · none
20
citations
affno abstractunlabeled
Looking for inhibition of return in pigeons
Brett M. Gibson, Igor Juricevic, Sara J. Shettleworth, Jay Pratt, Raymond M. Klein
2005· article· en· Learning & Behavior· Agricultural and Biological Sciences
machine prediction:candidate · noneconsensus · none
15
citations
affno abstractunlabeled
Assessing power PC
Lorraine G. Allan
2003· review· en· Learning & Behavior· Psychology
machine prediction:candidate · noneconsensus · none
14
citations
affno abstractunlabeled
Latent inhibition of conditioned disgust reactions in rats
Patricia Gasalla, Mercedes Vega-Villar, Cheryl L. Limebeer, EM Rock, Katharine J. Tuerke, H Bedard +1 more
2010· article· en· Learning & Behavior· Nursing
machine prediction:candidate · noneconsensus · none
11
citations
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How this was built: Screen · Findings · About