MétaCan
Menu
Cohort builder

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.

Search term
Author
Year range
Sort
Language
Type
Field
Venue
Topic
Stochastic Gradient Optimization Techniques
Retraction
Abstract
Evidence source
Study design
Label agreement
Label status

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.

affaffiliation
fundfunder
venuejournal
aboutaboutness

The four routes compose: require the funder route and exclude affiliation to get the funder-only stratum no affiliation-based frame ever sees.

271 results · 1 filter active ·
Results by year
20002025
Publication date
Categories
Machine labels · sparse coverage
Evidence
Language
Type
Citations
An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
271 works in the cohort · of 4,299,418page 4 of 6

Labels cover 1 of 271 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 271 of 271 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
Sign bit is enough
Feijie Wu, Shiqi He, Song Guo, Zhihao Qu, Haozhao Wang, Weihua Zhuang +1 more
2022· article· en· Proceedings of the 59th ACM/IEEE Design Automation Conference· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
fundno affunlabeled
Finding planted cliques using gradient descent
Reza Gheissari, Aukosh Jagannath, Yiming Xu
2023· preprint· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Convex Two-Layer Modeling with Latent Structure
Vignesh Ganapathiraman, Xinhua Zhang, Yaoliang Yu, Junfeng Wen
2016· article· en· Neural Information Processing Systems· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Anytime Minibatch With Delayed Gradients
2020· article· en· IEEE Transactions on Signal and Information Processing over Networks· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
JuliaNLSolvers/Optim.jl: v1.2.1
Patrick Kofod Mogensen, J.M. White, Asbjørn Nilsen Riseth, Tim Holy, Miles Lubin, Christof Stocker +23 more
2020· article· en· Open MIND· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
1
citations
fundno affunlabeled
Target-based Surrogates for Stochastic Optimization
Jonathan Wilder Lavington, Sharan Vaswani, Reza Babanezhad, Mark Schmidt, Nicolas Le Roux
2023· preprint· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About