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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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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.

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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
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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 1 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.

afffundunlabeled
MLCM: Multi-Label Confusion Matrix
Mohammadreza Heydarian, Thomas E. Doyle, Reza Samavi
2022· article· en· IEEE Access· Computer Science
machine prediction:candidate · noneconsensus · none
409
citations
affunlabeled
Naïve Bayes Text Classifier
Haiyi Zhang, Di Li
2007· article· en· 2007 IEEE International Conference on Granular Computing (GRC 2007)· Computer Science
machine prediction:candidate · noneconsensus · none
98
citations
affno abstractunlabeled
Beam search algorithms for multilabel learning
Abhishek Kumar, Shankar Vembu, Aditya Krishna Menon, Charles Elkan
2013· article· en· Machine Learning· Computer Science
machine prediction:candidate · noneconsensus · none
90
citations
fundno affno abstractunlabeled
A three-phase method for patent classification
Yen‐Liang Chen, Yuan-Che Chang
2012· article· en· Information Processing & Management· Computer Science
machine prediction:candidate · noneconsensus · none
60
citations
affno abstractunlabeled
Effective text classification using BERT, MTM LSTM, and DT
Saman Jamshidi, Mahin Mohammadi, Saeed Bagheri, Hamid Esmaeili Najafabadi, Alireza Rezvanian, Mehdi Gheisari +3 more
2024· article· en· Data & Knowledge Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
56
citations
affunlabeled
Shortest-Path Graph Kernels for Document Similarity
Giannis Nikolentzos, Polykarpos Meladianos, François Rousseau, Yannis Stavrakas, Michalis Vazirgiannis
2017· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
54
citations
afffundunlabeled
Partial Label Learning with Batch Label Correction
Yan Yan, Yuhong Guo
2020· article· en· Proceedings of the AAAI Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
50
citations
affno abstractunlabeled
Bayesian Citation-KNN with distance weighting
Liangxiao Jiang, Zhihua Cai, Dianhong Wang, Harry Zhang
2013· article· en· International Journal of Machine Learning and Cybernetics· Computer Science
machine prediction:candidate · noneconsensus · none
41
citations
affno abstractunlabeled
Label distribution learning: A local collaborative mechanism
Suping Xu, Hengrong Ju, Lin Shang, Witold Pedrycz, Xibei Yang, Chun Li
2020· article· en· International Journal of Approximate Reasoning· Computer Science
machine prediction:candidate · noneconsensus · none
41
citations
affno abstractunlabeled
Semi-supervised Multi-label Classification
Yuhong Guo, Dale Schuurmans
2012· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
33
citations
venueno affunlabeled
Arabic Text Classification: A Review
Adel Hamdan Mohammad
2019· review· en· Modern Applied Science· Computer Science
machine prediction:candidate · noneconsensus · none
28
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
25
citations

How this was built: Screen · Findings · About