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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.

80 results · 1 filter active ·
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20132021
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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.
80 works in the cohort · of 4,299,418page 2 of 2

Labels cover 0 of 80 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 80 of 80 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
DOM-Q-NET: Grounded RL on Structured Language
Sheng Jia, Jamie Kiros, Jimmy Ba
2019· article· en· International Conference on Learning Representations· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affunlabeled
Decoupling the Layers in Residual Networks
Ricky Fok, Aijun An, Zana Rashidi, Xiaogang Wang
2018· article· en· International Conference on Learning Representations· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Recurrent Normalization Propagation
César Laurent, Nicolas Ballas, Pascal Vincent
2017· article· en· International Conference on Learning Representations· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
SELF-INFORMED NEURAL NETWORK STRUCTURE LEARNING
David Warde-Farley, Andrew Rabinovich, Dragomir Anguelov
2015· article· en· International Conference on Learning Representations· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Reconstructing evolutionary trajectories of mutations in cancer
Yulia Rubanova, Ruian Shi, Roujia Li, Jeff Wintersinger, Amit G. Deshwar, Nil Sahin +1 more
2018· article· en· International Conference on Learning Representations· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
C-Learning: Horizon-Aware Cumulative Accessibility Estimation
Panteha Naderian, Gabriel Loaiza-Ganem, Harry J. Braviner, Anthony L. Caterini, Jesse C. Cresswell, Tong Li +1 more
2021· article· en· International Conference on Learning Representations· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Online variance-reducing optimization
Nicolas Le Roux, Reza Babanezhad, Pierre-Antoine Manzagol
2018· article· en· International Conference on Learning Representations· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
How Chaotic Are Recurrent Neural Networks
Pourya Vakilipourtakalou, Lili Mou
2020· article· en· International Conference on Learning Representations· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
I❤LA: Compilable Markdown for Linear Algebra
Yong Li, Shoaib Kamil, Alec Jacobson, Yotam Gingold
2021· article· en· International Conference on Learning Representations· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations

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