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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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Big Data and Business Intelligence
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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.

1,633 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.
1,633 works in the cohort · of 4,299,418page 33 of 33

Labels cover 10 of 1,633 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 1,633 of 1,633 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.

venueno affunlabeled
Editorial: Celebrating the World of Innovation
Stoyan Tanev, Gregory Sandstrom
2021· editorial· en· Technology Innovation Management Review· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Mobile Enterprise Data Discovery
William P. Smith, Helen Sun
2015· book-chapter· en· Apress eBooks· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Deploying SharePoint 2016
Vlad Catrinescu, Trevor Seward
2016· book· en· Apress eBooks· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Working with Semistructured Data
Dmitry Anoshin, Dmitry Shirokov, Donna Strok
2019· book-chapter· en· Apress eBooks· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
0
citations
affvenueunlabeled
Data Sustainability in Manufacturing Engineering Education
Christoph Sielmann, Nima Bakhshi
2024· article· en· Proceedings of the Canadian Engineering Education Association (CEEA)· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Le « Big Data » : opportunité et prudence
Anaïs Thibault-Landry, Lara Manganelli, Kathleen Bentein
2023· article· fr· Humain et Organisation· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
A Mathematical Model for Treatment Selection Literature
G. Duncan, Waldemar W. Koczkodaj
2015· book-chapter· en· Springer proceedings in mathematics & statistics· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
0
citations
venueno affunlabeled
Editorial: Insights (September 2018)
Chris McPhee
2018· editorial· en· Technology Innovation Management Review· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
THE IMPORTANCE OF DOCTORATE IN INFIRMARY SCIENCES
Judith M Scaular
2008· article· es· Revista Habanera de Ciencias Médicas· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Business analytics trends and opportunities
Craig Statchuk
2011· article· en· Conference of the Centre for Advanced Studies on Collaborative Research· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
0
citations
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