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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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Personal Information Management and User Behavior
Retraction
Abstract
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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.

465 results · 1 filter active ·
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20002025
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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.
465 works in the cohort · of 4,299,418page 2 of 10

Labels cover 5 of 465 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 465 of 465 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
Privacy in the open
Jeremy Birnholtz, Carl Gutwin, Kirstie Hawkey
2007· article· en· Decision Sciences
machine prediction:candidate · noneconsensus · none
45
citations
affunlabeled
eyeLook
Connor Dickie, Roel Vertegaal, Changuk Sohn, Daniel Cheng
2005· article· en· Decision Sciences
machine prediction:candidate · noneconsensus · none
37
citations
affunlabeled
Dealing With Task Interruptions in Complex Dynamic Environments
Sébastien Tremblay, François Vachon, Daniel Lafond, Chelsea Kramer
2011· article· en· Human Factors The Journal of the Human Factors and Ergonomics Society· Decision Sciences
machine prediction:candidate · noneconsensus · none
35
citations
affunlabeled
Recovering Context After Interruption
Jerry L. Franke, Jody J. Daniels, Daniel C. McFarlane
2019· book-chapter· en· Decision Sciences
machine prediction:candidate · noneconsensus · none
35
citations
afffundunlabeled
An exploration of web-based monitoring
Melanie Kellar, Carolyn Watters, Kori Inkpen
2007· article· en· Decision Sciences
machine prediction:candidate · noneconsensus · none
33
citations
affunlabeled
Going with the flow
Nelson Siu, Lee Iverson, Anthony Tang
2006· article· en· Decision Sciences
machine prediction:candidate · insufficient_payloadconsensus · none
32
citations
affunlabeled
Focusing knowledge work with task context
Mik Kersten
2011· article· en· cIRcle (University of British Columbia)· Decision Sciences
machine prediction:candidate · noneconsensus · none
32
citations
afffundunlabeled
InterTwine
Adam Fourney, Ben Lafreniere, Parmit K. Chilana, Michael Terry
2014· article· en· Decision Sciences
machine prediction:candidate · noneconsensus · none
29
citations
afffundunlabeled
Keeping up appearances
Kirstie Hawkey, Kori Inkpen
2006· article· en· Decision Sciences
machine prediction:candidate · noneconsensus · none
25
citations
affunlabeled
Trust in design
Stephen Marsh, John F. Meech
2000· article· en· Decision Sciences
machine prediction:candidate · noneconsensus · none
24
citations
affunlabeled
Countdown Timer Speed
Moojan Ghafurian, David Reitter, Frank E. Ritter
2020· article· en· ACM Transactions on Computer-Human Interaction· Decision Sciences
machine prediction:candidate · noneconsensus · none
23
citations
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How this was built: Screen · Findings · About