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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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Text and Document Classification Technologies
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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.

374 results · 1 filter active ·
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20002025
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Machine labels · sparse coverage
Evidence
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
374 works in the cohort · of 4,299,418page 4 of 8

Labels cover 2 of 374 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 374 of 374 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
Recommendation System
Reda Alhajj, Jon Rokne
2018· book-chapter· en· Computer Science
machine prediction:candidate · noneconsensus · none
7
citations
affunlabeled
Model Based Document Classification and Clustering
Alejandro Murua, Werner Stuetzle, Jeremy Tantrum, Solveig K. Sieberts
2008· article· en· International journal of tomography and simulation· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
fundno affno abstractunlabeled
Advances in Information Retrieval
Jaap Kamps, Lorraine Goeuriot, Fábio Crestani, Maria Maistro, Hideo Joho, Brian Davis +3 more
2023· book· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
5
citations
affunlabeled
Feature ranking fusion for text classifier
Masoud Makrehchi, Mohamed S. Kamel
2012· article· en· Intelligent Data Analysis· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affno abstractunlabeled
A Best Match KNN-based Approach for Large-scale Product Categorization.
Haohao Hu, Runjie Zhu, Yuqi Wang, Wenying Feng, Xing Tan, Jimmy Xiangji Huang
2018· article· en· International ACM SIGIR Conference on Research and Development in Information Retrieval· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affno abstractunlabeled
Text Categorization via Similarity Search
Hubert Haoyang Duan, Vladimir Pestov, Varun Singla
2013· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affno abstractunlabeled
Document Classification Using Phrases
Jan Bakus, Mohamed S. Kamel
2002· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
Classifying Websites into Non-topical Categories
Chaman Thapa, Osmar R. Zai͏̈ane, Davood Rafiei, Arya M. Sharma
2012· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
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How this was built: Screen · Findings · About