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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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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.

127 results · 1 filter active ·
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20022021
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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.
127 works in the cohort · of 4,299,418page 1 of 3

Labels cover 0 of 127 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 127 of 127 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
On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, Kilian Q. Weinberger
2017· article· en· International Conference on Machine Learning· Computer Science
machine prediction:candidate · noneconsensus · none
1,185
citations
affno abstractunlabeled
Mutual Information Neural Estimation.
Mohamed Ishmael Belghazi, Aristide Baratin, Sai Rajeshwar, Sherjil Ozair, Yoshua Bengio, Aaron Courville +1 more
2018· article· en· International Conference on Machine Learning· Computer Science
machine prediction:candidate · noneconsensus · none
598
citations
affunlabeled
Invertible Residual Networks
Jens Behrmann, Will Grathwohl, Ricky T. Q. Chen, David Duvenaud, Joern-Henrik Jacobsen
2019· article· en· International Conference on Machine Learning· Computer Science
machine prediction:candidate · noneconsensus · none
193
citations
affno abstractunlabeled
Deep Spectral Clustering Learning.
Marc T. Law, Raquel Urtasun, Richard S. Zemel
2017· article· en· International Conference on Machine Learning· Computer Science
machine prediction:candidate · noneconsensus · none
107
citations
affunlabeled
Understanding the Origins of Bias in Word Embeddings
Marc-Etienne Brunet, Colleen Alkalay-Houlihan, Ashton Anderson, Richard S. Zemel
2019· article· en· International Conference on Machine Learning· Computer Science
machine prediction:candidate · noneconsensus · none
58
citations
affno abstractunlabeled
Unreproducible Research is Reproducible
Xavier Bouthillier, César Laurent, Pascal Vincent
2019· article· en· International Conference on Machine Learning· Decision Sciences
machine prediction:candidate · metaresearchconsensus · none
57
citations
affunlabeled
Environment Inference for Invariant Learning
Elliot Creager, Joern-Henrik Jacobsen, Richard S. Zemel
2021· article· en· International Conference on Machine Learning· Computer Science
machine prediction:candidate · noneconsensus · none
51
citations
affunlabeled
Deep Supervised t-Distributed Embedding
Martin Renqiang Min, L.J.P. van der Maaten, Zineng Yuan, Anthony J. Bonner, Zhaolei Zhang
2010· article· en· International Conference on Machine Learning· Computer Science
machine prediction:candidate · noneconsensus · none
48
citations
affunlabeled
Tensor Analyzers
Yichuan Tang, Ruslan Salakhutdinov, Geoffrey E. Hinton
2013· article· en· International Conference on Machine Learning· Mathematics
machine prediction:candidate · noneconsensus · none
41
citations
affno abstractunlabeled
True Online TD(lambda)
Harm van Seijen, Rich Sutton
2014· article· en· International Conference on Machine Learning· Computer Science
machine prediction:candidate · noneconsensus · none
30
citations
affunlabeled
Adaptive Consensus ADMM for Distributed Optimization
Zheng Xu, Gavin Taylor, Hao Li, Mário A. T. Figueiredo, Xiaoming Yuan, Tom Goldstein
2017· article· en· International Conference on Machine Learning· Computer Science
machine prediction:candidate · noneconsensus · none
21
citations
affunlabeled
Clustering in the Presence of Background Noise
Shai Ben-David, Nika Haghtalab
2014· article· en· International Conference on Machine Learning· Computer Science
machine prediction:candidate · noneconsensus · none
21
citations

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