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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.

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.

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.

1,935 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.
1,935 works in the cohort · of 4,299,418page 16 of 39

Labels cover 22 of 1,935 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 1,935 of 1,935 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
Hierarchical Importance Weighted Autoencoders
Chin-Wei Huang, Kris Sankaran, Eeshan Gunesh Dhekane, Alexandre Lacoste, Aaron Courville
2019· article· en· arXiv (Cornell University)· Mathematics
machine prediction:candidate · noneconsensus · none
6
citations
afffundunlabeled
A nonparametric approach for quantile regression
Mei Ling Huang, Christine Nguyen
2018· article· en· Journal of Statistical Distributions and Applications· Mathematics
machine prediction:candidate · noneconsensus · none
6
citations
affunlabeled
Cramér‐von Mises tests for change points
Rasmus Erlemann, Richard Lockhart, Rihan Yao
2021· article· en· Scandinavian Journal of Statistics· Mathematics
machine prediction:candidate · noneconsensus · none
6
citations
afffundno abstractunlabeled
Group penalized quantile regression
Mohamed Ouhourane, Yi Yang, Andréa Lessa Benedet, Karim Oualkacha
2021· article· en· Statistical Methods & Applications· Mathematics
machine prediction:candidate · noneconsensus · none
6
citations
affunlabeled
Unconditional Quantile Regressions
Sérgio Firpo, Nicole M. Fortin, Thomas Lemieux
2007· preprint· en· National Bureau of Economic Research· Mathematics
machine prediction:candidate · noneconsensus · none
6
citations
afffundunlabeled
On Goodness of Fit for Operational Risk
Andrey Feuerverger
2015· article· en· International Statistical Review· Mathematics
machine prediction:candidate · noneconsensus · none
6
citations
affunlabeled
Bayesian Additive Regression Trees, Computational Approaches
Hugh Chipman, Edward I. George, R. L. Hahn, Robert McCulloch, Matthew T. Pratola, Rodney Sparapani
2022· other· en· Wiley StatsRef: Statistics Reference Online· Mathematics
machine prediction:candidate · noneconsensus · none
6
citations
affno abstractunlabeled
Modeling Multivariate Data Revisions
Jan Jacobs, Samad Sarferaz, Jan‐Egbert Sturm, Simon van Norden
2013· article· en· SSRN Electronic Journal· Mathematics
machine prediction:candidate · noneconsensus · none
6
citations
aboutno affno abstractunlabeled
The Bootstrap Method
Kirk M. Wolter
2007· book-chapter· en· Statistics for social and behavioral sciences· Mathematics
machine prediction:candidate · noneconsensus · none
6
citations

How this was built: Screen · Findings · About