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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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Statistical Methods 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.

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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.

137 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.
137 works in the cohort · of 4,299,418page 1 of 3

Labels cover 1 of 137 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 137 of 137 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
FORWARD SELECTION OF EXPLANATORY VARIABLES
F. Guillaume Blanchet, Pierre Legendre, Daniel Borcard
2008· article· en· Ecology· Mathematics
machine prediction:candidate · noneconsensus · none
2,123
citations
affno abstractunlabeled
The curse(s) of dimensionality
Naomi Altman, Martin Krzywinski
2018· article· en· Nature Methods· Mathematics
machine prediction:candidate · noneconsensus · none
477
citations
affno abstractunlabeled
Classification and regression trees
Martin Krzywinski, Naomi Altman
2017· article· en· Nature Methods· Mathematics
machine prediction:candidate · noneconsensus · none
382
citations
affno abstractunlabeled
Machine learning: supervised methods
Danilo Bzdok, Martin Krzywinski, Naomi Altman
2018· article· en· Nature Methods· Mathematics
machine prediction:candidate · noneconsensus · none
337
citations
venueno affunlabeled
A Truly Multivariate Approach to Manova
James W. Grice, Michiko Iwasaki
2009· article· en· Applied Multivariate Research· Mathematics
machine prediction:candidate · noneconsensus · none
140
citations
affno abstractunlabeled
P values and the search for significance
Naomi Altman, Martin Krzywinski
2016· article· en· Nature Methods· Mathematics
machine prediction:candidate · metaresearchconsensus · none
134
citations
affno abstractunlabeled
Convolutional neural networks
Alexander Derry, Martin Krzywinski, Naomi Altman
2023· article· en· Nature Methods· Mathematics
machine prediction:candidate · noneconsensus · none
97
citations
affno abstractunlabeled
Comparing classifier performance with baselines
Fadel M. Megahed, Ying‐Ju Chen, L. Allison Jones‐Farmer, Steven E. Rigdon, Martin Krzywinski, Naomi Altman
2024· article· en· Nature Methods· Mathematics
machine prediction:candidate · metaresearchconsensus · none
7
citations
aboutno affunlabeled
Statistiques. Concepts et applications Ed. 2
Robert R. Haccoun, Denis Cousineau
2010· book· fr· Presses de l'Université de Montréal PUM eBooks· Mathematics
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Comparing groups using analysis of variance
Janith Weeraman, Qingrun Zhang
2025· book-chapter· en· Elsevier eBooks· Mathematics
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
cheshmi/sc23-ad-sparse-fusion: v1.0.1
2023· other· en· Zenodo (CERN European Organization for Nuclear Research)· Mathematics
machine prediction:candidate · noneconsensus · none
0
citations
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