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
Neural Networks and Reservoir Computing
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.

516 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.
516 works in the cohort · of 4,299,418page 5 of 11

Labels cover 0 of 516 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 516 of 516 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.

afffundunlabeled
A 3D‐Printed Computer
Vahideh Shirmohammadli, Behraad Bahreyni
2023· article· en· Advanced Intelligent Systems· Computer Science
machine prediction:candidate · noneconsensus · none
5
citations
affunlabeled
Introducing SPECS: scalable photonic event-driven circuit simulator
Clément Zrounba, Rodrigo Perito Cardoso, Maurício Gomes de Queiroz, Pedro Jiménez‐Guerrero, M. I. Abdalla, Alberto Bosio +3 more
2023· article· en· IET conference proceedings.· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
fundno affunlabeled
Differentiable reservoir computing
Lyudmila Grigoryeva, Juan‐Pablo Ortega
2019· preprint· en· Alexandria (UniSG) (University of St.Gallen)· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affunlabeled
Autaptic Circuits of Integrated Laser Neurons
Hsuan-Tung Peng, Thomas Ferreira de Lima, Mitchell A. Nahmias, Alexander N. Tait, Bhavin J. Shastri, Paul R. Prucnal
2019· article· en· Conference on Lasers and Electro-Optics· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affunlabeled
Loss-Free Excitonic Quantum Battery
Junjie Liu, Dvira Segal, Gabriel Hanna
2019· article· en· The Journal of Physical Chemistry· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affno abstractunlabeled
Quantum Brain Dynamics and Virtual Reality
Akihiro Nishiyama, Shigenori Tanaka, Jack A. Tuszyński
2024· article· en· Biosystems· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
afffundunlabeled
Coherent feed-forward quantum neural network
Utkarsh Singh, Aaron Z. Goldberg, Khabat Heshami
2024· article· en· Quantum 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

How this was built: Screen · Findings · About