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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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Efficiency Analysis Using DEA
Retraction
Abstract
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Label agreement
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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.

813 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.
813 works in the cohort · of 4,299,418page 1 of 17

Labels cover 6 of 813 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 813 of 813 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
Additive efficiency decomposition in two-stage DEA
Yao Chen, Wade D. Cook, Ning Li, Joe Zhu
2008· article· en· European Journal of Operational Research· Decision Sciences
machine prediction:candidate · noneconsensus · none
684
citations
affno abstractunlabeled
DEA models for supply chain efficiency evaluation
Liang Liang, Feng Yang, Wade D. Cook, Joe Zhu
2006· article· en· Annals of Operations Research· Decision Sciences
machine prediction:candidate · noneconsensus · none
462
citations
aboutno affunlabeled
Department of Obstetrics and Gynecology
2015· article· en· Juntendo Medical Journal· Decision Sciences
machine prediction:candidate · insufficient_payloadconsensus · none
367
citations
affno abstractunlabeled
Network DEA: Additive efficiency decomposition
Wade D. Cook, Joe Zhu, Gongbing Bi, Feng Yang
2010· article· en· European Journal of Operational Research· Decision Sciences
machine prediction:candidate · noneconsensus · none
323
citations
affno abstractunlabeled
Deriving the DEA frontier for two-stage processes
Yao Chen, Wade D. Cook, Joe Zhu
2009· article· en· European Journal of Operational Research· Decision Sciences
machine prediction:candidate · noneconsensus · none
223
citations
affunlabeled
Data Envelopment Analysis with Nonhomogeneous DMUs
Wade D. Cook, Julie Harrison, Raha Imanirad, Paul Rouse, Joe Zhu
2013· article· en· Operations Research· Decision Sciences
machine prediction:candidate · noneconsensus · none
109
citations
affno abstractunlabeled
Data envelopment analysis and big data
Dariush Khezrimotlagh, Joe Zhu, Wade D. Cook, Mehdi Toloo
2018· article· en· European Journal of Operational Research· Decision Sciences
machine prediction:candidate · noneconsensus · none
98
citations
affno abstractunlabeled
Measuring Inefficiency Via Potential Improvements
Mette Asmild, Jens Leth Hougaard, Dorte Kronborg, Hans Kurt Kvist
2003· article· en· Journal of Productivity Analysis· Decision Sciences
machine prediction:candidate · noneconsensus · none
79
citations
affunlabeled
Putting Out The Trash
Adrian T. Moore, James Nolan, Geoffrey Segal
2005· article· en· Urban Affairs Review· Decision Sciences
machine prediction:candidate · noneconsensus · none
73
citations
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How this was built: Screen · Findings · About