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
Risk and Portfolio Optimization
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.

710 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.
710 works in the cohort · of 4,299,418page 4 of 15

Labels cover 2 of 710 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 710 of 710 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.

afffundno abstractunlabeled
Multiportfolio optimization with CVaR risk measure
Guoqing Zhang, Qiqi Zhang
2019· article· en· Journal of Data Information and Management· Decision Sciences
machine prediction:candidate · noneconsensus · none
16
citations
afffundunlabeled
Portfolio Optimization within a Wasserstein Ball
Silvana M. Pesenti, Sebastian Jaimungal
2023· article· en· SIAM Journal on Financial Mathematics· Decision Sciences
machine prediction:candidate · noneconsensus · none
15
citations
affunlabeled
Convolution Bounds on Quantile Aggregation
José Blanchet, Henry Lam, Yang Liu, Ruodu Wang
2024· article· en· Operations Research· Decision Sciences
machine prediction:candidate · noneconsensus · none
14
citations
affno abstractunlabeled
Optimal insurance under maxmin expected utility
Corina Birghila, Tim J. Boonen, Mario Ghossoub
2023· article· en· Finance and Stochastics· Decision Sciences
machine prediction:candidate · noneconsensus · none
14
citations
afffundno abstractunlabeled
On aggregation sets and lower-convex sets
Tiantian Mao, Ruodu Wang
2014· article· en· Journal of Multivariate Analysis· Decision Sciences
machine prediction:candidate · noneconsensus · none
14
citations
afffundno abstractunlabeled
Risk measures on the space of infinite sequences
Hirbod Assa, Manuel Morales
2010· article· en· Mathematics and Financial Economics· Decision Sciences
machine prediction:candidate · noneconsensus · none
13
citations
afffundno abstractunlabeled
A robust framework for risk parity portfolios
Giorgio Costa, Roy H. Kwon
2020· article· en· Journal of Asset Management· Decision Sciences
machine prediction:candidate · noneconsensus · none
13
citations
affno abstractunlabeled
General Extremal Dependence Concepts
Giovanni Puccetti, Ruodu Wang
2014· article· en· SSRN Electronic Journal· Decision Sciences
machine prediction:candidate · noneconsensus · none
12
citations
affunlabeled
Life after VaR
Phelim P. Boyle, Mary R. Hardy, Ton Vorst
2005· article· en· The Journal of Derivatives· Decision Sciences
machine prediction:candidate · noneconsensus · none
12
citations

How this was built: Screen · Findings · About