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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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Information Retrieval and Search Behavior
Retraction
Abstract
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Label status

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
fundfunder
venuejournal
aboutaboutness

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
Language
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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 9 of 10

Labels cover 3 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.

affno abstractunlabeled
Relevance Feedback for Text Retrieval
Olga Vechtomova
2016· book-chapter· en· Encyclopedia of Database Systems· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Document Retrieval
University Research Chair
2009· book-chapter· en· Encyclopedia of Database Systems· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Information Retrieval Operations
Edie Rasmussen
2016· book-chapter· en· Encyclopedia of Database Systems· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Webnotes: Is Google Getting Too Good?
Bill Orr
2007· article· en· ABA banking journal· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Readers' Queries
R. Preston
2006· article· en· Notes and Queries· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
venueno affunlabeled
Current research
Rebecca Zakoor
2004· article· fr· Journal of the Canadian Health Libraries Association / Journal de l Association de bilbiothèques de la santé du Canada· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affno abstractunlabeled
Information Retrieval Operations
2009· book-chapter· en· Encyclopedia of Database Systems· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affno abstractunlabeled
Selective Search as a First-Stage Retriever
Gijs Hendriksen, Djoerd Hiemstra, Arjen P. de Vries
2025· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
fundno affunlabeled
University of Amsterdam at the CLEF 2023 SimpleText Track
R. Hutter, J. Sutmuller, M. Adib, D. Rau, J.; id_orcid 0000-0002-6614-0087 Kamps
2023· article· en· UvA-DARE (University of Amsterdam)· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Do Subtopic Judgments Reflect Diversity?
John A. Akinyemi, Charles L. A. Clarke
2011· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
venueaboutno affunlabeled
Magnitude Feedback During Database Searching
Amanda Spink
2013· article· en· Proceedings of the Annual Conference of CAIS / Actes du congrès annuel de l ACSI· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
fundno affunlabeled
Recall Aspects of Transformers for Text Ranking
D. Rau, J.; id_orcid 0000-0002-6614-0087 Kamps
2022· article· en· UvA-DARE (University of Amsterdam)· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
venueno affunlabeled
Exploring the Realities of Interaction and Search Success
Elisabeth Logan, Kristen Jacobson
2013· article· en· Proceedings of the Annual Conference of CAIS / Actes du congrès annuel de l ACSI· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affvenueunlabeled
Getting the whole story
Kydra Mayhew, Maria Henkel, Geoff Krause, L Morrison, Courtney Svab, Philippe Mongeon
2022· article· en· Proceedings of the Annual Conference of CAIS / Actes du congrès annuel de l ACSI· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
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