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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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Machine Learning and Data Classification
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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.

559 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.
559 works in the cohort · of 4,299,418page 9 of 12

Labels cover 1 of 559 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 559 of 559 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.

affunlabeled
Two Case Studies
Nathalie Japkowicz, Mohak Shah
2011· other· en· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affunlabeled
Estimated code
Xiang Li, Lulu Song, Qiance Liu, Xin Ouyang, Ting Mao, Haojie Lu +4 more
2023· dataset· en· Figshare· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affno abstractunlabeled
A Quest for AI Knowledge
Joshua Gans
2025· article· en· SSRN Electronic Journal· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Dataset - variable selection
Barbara Vuillaume, Julien H. Richard, Sandra Hamel, Joëlle Taillon, Marco Festa‐Bianchet, Steeve D. Côté
2023· dataset· en· Figshare· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Program
Sreeraman Rajan, Khaled A. Helal Kelany, Clemens P. J. Adolphs, Amirali Baniasadi, Ian Goode, Carlos E. Saavedra
2022· article· en· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
aboutno affunlabeled
Stenus (Nestus) brivioi Puthz 1972
2012· article· en· Zenodo (CERN European Organization for Nuclear Research)· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
venueno affunlabeled
Investigating the Interaction between Data and Algorithms
Daniel Pototzky, Azhar Sultan, Lars Schmidt-Thieme
2022· article· en· Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Task-Informed Meta-Learning data
Gabriel Tseng
2022· dataset· en· Zenodo (CERN European Organization for Nuclear Research)· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Countwordserror
Jake Lever
2017· dataset· en· Zenodo (CERN European Organization for Nuclear Research)· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affunlabeled
Task-Informed Meta-Learning data
Gabriel Tseng
2022· dataset· en· Zenodo (CERN European Organization for Nuclear Research)· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Large-Scale Fully-Unsupervised Re-Identification
Gabriel Bertocco, Fernanda Andaló, Terrance E. Boult, Anderson Rocha
2024· article· en· IEEE Transactions on Biometrics Behavior and Identity Science· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
ChaLearn AutoML Challenges
Hugo Jair Escalante, Jorge Madrid, Eduardo A. Morales
2018· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
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