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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.

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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.

2,372 results · 1 filter active ·
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20002025
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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.
2,372 works in the cohort · of 4,299,418page 28 of 48

Labels cover 1 of 2,372 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 2,372 of 2,372 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
Gradient Descent Resists Compositionality
Yuanpeng Li, Liang Zhao, Joel Hestness, Kenneth Church, Mohamed Elhoseiny
2021· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
afffundunlabeled
Learning the structure of abstract groups
Dirk Schlimm, Thomas R. Shultz
2009· article· en· eScholarship (California Digital Library)· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Forecasting SNR margins has low-complexity
Firouzeh Golaghazadeh, Petar Djukic, Christine Tremblay, Christian Desrosiers
2020· article· en· OSA Advanced Photonics Congress (AP) 2020 (IPR, NP, NOMA, Networks, PVLED, PSC, SPPCom, SOF)· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
How Chaotic Are Recurrent Neural Networks?
Pourya Vakilipourtakalou, Lili Mou
2020· preprint· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Object Classification
D. Sundararajan
2017· book-chapter· en· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Ensemble Learning
Hui Jiang
2021· book-chapter· en· Cambridge University Press eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Smart neural nets for fast learning
B.W. Dahanayake, A.R.M. Upton
2002· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
fundno affunlabeled
[no title]
Sónia Matos, Matthew Fuller
2011· article· en· Edinburgh Research Explorer· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
1
citations
affno abstractunlabeled
Set oriented mappings on neural networks
Roelof K. Brouwer, Witold Pedrycz
2003· article· en· Soft Computing· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
venueno affunlabeled
Complexity Analysis in Heterogeneous System
Kuldeep Sharma, Deepak Garg
2009· article· en· Computer and Information Science· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
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