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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
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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
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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 3 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
Increasing evaluation sensitivity to diversity
Peter B. Golbus, Javed A. Aslam, Charles L. A. Clarke
2013· article· en· Information Retrieval· Computer Science
machine prediction:candidate · noneconsensus · none
22
citations
affunlabeled
Overview of the TREC 2012 Contextual Suggestion Track.
Adriel Dean-Hall, Charles L. A. Clarke, Jaap Kamps, Paul Thomas, Ellen M Voorhes
2012· article· en· Text REtrieval Conference· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
22
citations
affunlabeled
Research perspectives on serendipity and information encountering
Sanda Erdelez, Jannica Heinström, Stephann Makri, Lennart Björneborn, Jamshid Beheshti, Elaine G. Toms +1 more
2016· article· en· Proceedings of the Association for Information Science and Technology· Computer Science
machine prediction:candidate · noneconsensus · none
21
citations
fundno affno abstractunlabeled
Advances in Information Retrieval
2024· book· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
21
citations
affno abstractunlabeled
Information Retrieval
2018· book-chapter· en· Computer Science
machine prediction:candidate · noneconsensus · none
20
citations
afffundunlabeled
Effective User Interaction for High-Recall Retrieval
Haotian Zhang, Mustafa Abualsaud, Nimesh Ghelani, Mark D. Smucker, Gordon V. Cormack, Maura R. Grossman
2018· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
20
citations
affunlabeled
Characterizing commercial intent
Azin Ashkan, Charles L. A. Clarke
2009· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
19
citations
afffundunlabeled
Knowledge Graphs versus Hierarchies
Bahareh Sarrafzadeh, Alexandra Vtyurina, Edward Lank, Olga Vechtomova
2016· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
18
citations
fundno affno abstractunlabeled
Advances in Information Retrieval
2024· book· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
17
citations
affunlabeled
Supporting the Modern Polyglot
Ben Steichen, Luanne Freund
2015· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
17
citations
afffundunlabeled
Mouse movement during relevance judging
Mark D. Smucker, Xiaoyu Guo, Andrew Toulis
2014· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
16
citations
affunlabeled
Measuring assessor accuracy
Mark D. Smucker, Chandra Prakash Jethani
2011· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
15
citations
affno abstractunlabeled
Enhancing click models with mouse movement information
Zeyang Liu, Jiaxin Mao, Chao Wang, Qingyao Ai, Yiqun Liu, Jian‐Yun Nie
2017· article· en· Information Retrieval· Computer Science
machine prediction:candidate · noneconsensus · none
15
citations
affaboutunlabeled
Evolution of information practices over time
Devon Greyson
2016· article· en· Proceedings of the Association for Information Science and Technology· Computer Science
machine prediction:candidate · noneconsensus · none
13
citations
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