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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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Tensor decomposition and applications
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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.

affaffiliation
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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.

314 results · 1 filter active ·
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20002025
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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.
314 works in the cohort · of 4,299,418page 1 of 7

Labels cover 0 of 314 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 314 of 314 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
Learning with Pseudo-Ensembles
Phil Bachman, Ouais Alsharif, Doina Precup
2014· article· en· arXiv (Cornell University)· Mathematics
machine prediction:candidate · noneconsensus · none
361
citations
affunlabeled
Supervised Learning with Tensor Networks
E. Miles Stoudenmire, David J. Schwab
2016· article· en· Neural Information Processing Systems· Mathematics
machine prediction:candidate · noneconsensus · none
214
citations
affno abstractunlabeled
Higher secant varieties of the Segre varieties <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.gif" overflow="scroll"><mml:msup><mml:mrow><mml:mi mathvariant="double-struck">P</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msup><mml:mo>×</mml:mo><mml:mo>⋯</mml:mo><mml:mo>×</mml:mo><mml:msup><mml:mrow><mml:mi mathvariant="double-struck">P</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msup></mml:math>
Maria Virginia Catalisano, Anthony V. Geramita, Alessandro Gimigliano
2005· article· lv· Journal of Pure and Applied Algebra· Mathematics
machine prediction:candidate · noneconsensus · none
49
citations
affunlabeled
Tensor Analyzers
Yichuan Tang, Ruslan Salakhutdinov, Geoffrey E. Hinton
2013· article· en· International Conference on Machine Learning· Mathematics
machine prediction:candidate · noneconsensus · none
41
citations
afffundunlabeled
Learning Tensors From Partial Binary Measurements
Navid Ghadermarzy, Yaniv Plan, Özgür Yılmaz
2018· article· en· IEEE Transactions on Signal Processing· Mathematics
machine prediction:candidate · noneconsensus · none
37
citations
affunlabeled
Tensor-Based Adaptive Filtering Algorithms
Laura-Maria Dogariu, Cristian-Lucian Stanciu, Camelia Elisei-Iliescu, Constantin Paleologu, Jacob Benesty, Silviu Ciochină
2021· article· en· Symmetry· Mathematics
machine prediction:candidate · noneconsensus · none
32
citations
fundno affunlabeled
Prolongations and Computational Algebra
Jessica Sidman, Seth Sullivant
2009· article· en· Canadian Journal of Mathematics· Mathematics
machine prediction:candidate · noneconsensus · none
31
citations
affno abstractunlabeled
Multidimensional Scaling
Z. Zhang, Yoshio Takane
2010· book-chapter· en· Elsevier eBooks· Mathematics
machine prediction:candidate · noneconsensus · none
27
citations
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