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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.

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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Lecture notes in business information processing
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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.

198 results · 1 filter active ·
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20082025
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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.
198 works in the cohort · of 4,299,418page 1 of 4

Labels cover 0 of 198 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 198 of 198 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
Enterprise Modeling for Business Intelligence
Daniele Barone, Eric Yu, Jihyun Won, Lei Jiang, John Mylopoulos
2010· book-chapter· en· Lecture notes in business information processing· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
48
citations
affno abstractunlabeled
Design Requirements Engineering: A Ten-Year Perspective
Kalle Lyytinen, Bill Robinson, Pericles Loucopoulos, John Mylopoulos
2009· book· en· Lecture notes in business information processing· Computer Science
machine prediction:candidate · noneconsensus · none
46
citations
affno abstractunlabeled
Web 2.0 OLAP: From Data Cubes to Tag Clouds
Kamel Aouiche, Daniel Lemire, Robert Godin
2009· book-chapter· en· Lecture notes in business information processing· Computer Science
machine prediction:candidate · noneconsensus · none
22
citations
affno abstractunlabeled
Verification of Information Flow in Agent-Based Systems
Khair Eddin Sabri, Ridha Khédri, Jason Jaskolka
2009· book-chapter· en· Lecture notes in business information processing· Computer Science
machine prediction:candidate · noneconsensus · none
17
citations
affno abstractunlabeled
Analyzing IT Flexibility to Enable Dynamic Capabilities
Mohammad Hossein Danesh, Eric Yu
2015· book-chapter· en· Lecture notes in business information processing· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
16
citations
affno abstractunlabeled
Typing for Conflict Detection in Access Control Policies
Kamel Adi, Yacine Bouzida, Ikhlass Hattak, Luigi Logrippo, Serge Mankovskii
2009· book-chapter· en· Lecture notes in business information processing· Social Sciences
machine prediction:candidate · noneconsensus · none
15
citations
affno abstractunlabeled
How the Website Usability Elements Impact Performance
Muhammad Aljukhadar, Sylvain Sénécal
2009· book-chapter· en· Lecture notes in business information processing· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
12
citations

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