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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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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.

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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 4 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.

afffundgemma · scholarly_communicationgpt · scholarly_communicationmodels split
Constructing a true<scp>LCSH</scp>tree of a science and engineering collection
Charles‐Antoine Julien, Pierre Tirilly, John E. Leide, Catherine Guastavino
2012· article· en· Journal of the American Society for Information Science and Technology· Computer Science
machine prediction:candidate · scholarly_communicationconsensus · none
11
citations
afffundunlabeled
A vlHMM approach to context-aware search
Zhen Liao, Daxin Jiang, Jian Pei, Yalou Huang, Enhong Chen, Huanhuan Cao +1 more
2013· article· en· ACM Transactions on the Web· Computer Science
machine prediction:candidate · noneconsensus · none
11
citations
affno abstractunlabeled
Generative Information Retrieval Evaluation
Marwah Alaofi, Negar Arabzadeh, Charles L. A. Clarke, Mark Sanderson
2024· book-chapter· en· ˜The œinformation retrieval series· Computer Science
machine prediction:candidate · noneconsensus · none
9
citations
afffundno abstractunlabeled
The impact of text browsing on text retrieval performance
Richard C. Bodner, Mark Chignell, Nipon Charoenkitkarn, Gene Golovchinsky, Richard Kopak
2001· article· en· Information Processing & Management· Computer Science
machine prediction:candidate · noneconsensus · none
9
citations
afffundunlabeled
Modeling Optimal Switching Behavior
Mark D. Smucker, Charles L. A. Clarke
2016· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
8
citations
affunlabeled
Human-Centred Web Search
Orland Hoeber
2012· book-chapter· en· IGI Global eBooks· Computer Science
machine prediction:candidate · scholarly_communicationconsensus · none
8
citations
affno abstractunlabeled
Probability Smoothing
Djoerd Hiemstra
2009· book-chapter· en· Encyclopedia of Database Systems· Computer Science
machine prediction:candidate · noneconsensus · none
7
citations
affno abstractunlabeled
On Measuring Learning in Search: A Position Paper.
Luanne Freund, Samuel Dodson, Rick Kopak
2016· article· en· International ACM SIGIR Conference on Research and Development in Information Retrieval· Computer Science
machine prediction:candidate · metaresearchconsensus · none
6
citations
affno abstractunlabeled
Swapping documents and terms
Charles L. A. Clarke, Gordon V. Cormack, Thomas R. Lynam, Chris Buckley, Donna Harman
2009· article· en· Information Retrieval· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations

How this was built: Screen · Findings · About