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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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Nature Machine Intelligence
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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.

76 results · 1 filter active ·
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20182025
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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.
76 works in the cohort · of 4,299,418page 2 of 2

Labels cover 1 of 76 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 76 of 76 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
Aligning generalization between humans and machines
Filip Ilievski, Barbara Hammer, Frank van Harmelen, Benjamin Paaßen, Sascha Saralajew, Ute Schmid +19 more
2025· article· en· Nature Machine Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
11
citations
affno abstractunlabeled
Anniversary AI reflections
Noelia Ferruz, Marinka Žitnik, Pierre‐Yves Oudeyer, Emmie Hine, Nandana Sengupta, Yiyu Shi +4 more
2024· article· en· Nature Machine Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
11
citations
affno abstractunlabeled
Lossless data compression by large models
Ziguang Li, Xuliang Wang, Haibo Hu, Cole Wyeth, Dongbo Bu, Quan Yu +3 more
2025· article· en· Nature Machine Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
8
citations
affno abstractunlabeled
Geometric deep learning of particle motion by MAGIK
Bahare Fatemi, Jonathan Halcrow, Khuloud Jaqaman
2023· article· en· Nature Machine Intelligence· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
4
citations
afffundno abstractunlabeled
Variational neural annealing
Mohamed Hibat-Allah, Estelle M. Inack, Roeland Wiersema, Roger G. Melko, Juan Carrasquilla
2021· preprint· en· Nature Machine Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
Spatially embedded neuromorphic networks
Filip Milisav, Bratislav Mišić
2023· article· en· Nature Machine Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
Publisher Correction: Advancing ethics review practices in AI research
Madhulika Srikumar, Rebecca Finlay, Grace Abuhamad, Carolyn Ashurst, Rosie Campbell, Emily Campbell-Ratcliffe +5 more
2023· article· en· Nature Machine Intelligence· Medicine
machine prediction:candidate · metaresearch+research_integrityconsensus · none
2
citations
affno abstractunlabeled
Deciphering RNA–ligand binding specificity with GerNA-Bind
Yunpeng Xia, Yi-Ting Chu, Jiahua Rao, Jing Chen, Chenqing Hua, Dong‐Jun Yu +2 more
2025· article· en· Nature Machine Intelligence· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Moving beyond reward prediction errors
Blake A. Richards
2019· article· en· Nature Machine Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About