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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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Advanced Statistical Methods and Models
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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.

1,193 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.
1,193 works in the cohort · of 4,299,418page 2 of 24

Labels cover 6 of 1,193 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 1,193 of 1,193 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.

affno abstractunlabeled
Interpreting P values
Naomi Altman, Martin Krzywinski
2017· article· en· Nature Methods· Mathematics
machine prediction:candidate · metaresearchconsensus · none
91
citations
affno abstractunlabeled
Regression diagnostics
Naomi Altman, Martin Krzywinski
2016· article· en· Nature Methods· Mathematics
machine prediction:candidate · noneconsensus · none
90
citations
affno abstractunlabeled
Fast and robust bootstrap
Matías Salibián‐Barrera, Stefan Van Aelst, Gert Willems
2007· article· en· Statistical Methods & Applications· Mathematics
machine prediction:candidate · noneconsensus · none
82
citations
afffundno abstractunlabeled
Linear grouping using orthogonal regression
Stefan Van Aelst, Xiaogang Wang, Ruben H. Zamar, Rong Zhu
2004· article· en· Computational Statistics & Data Analysis· Mathematics
machine prediction:candidate · noneconsensus · none
77
citations
afffundunlabeled
On Some Principles of Statistical Inference
Nancy Reid, D. R. Cox
2014· article· en· International Statistical Review· Mathematics
machine prediction:candidate · metaresearchconsensus · none
72
citations
affunlabeled
Principal surfaces from unsupervised kernel regression
Peter Meinicke, Stefan Klanke, Roland Memisevic, Helge Ritter
2005· article· en· IEEE Transactions on Pattern Analysis and Machine Intelligence· Mathematics
machine prediction:candidate · noneconsensus · none
71
citations
affno abstractunlabeled
Robust projected clustering
Gabriela Moise, Jörg Sander, Martin Ester
2007· article· en· Knowledge and Information Systems· Mathematics
machine prediction:candidate · noneconsensus · none
70
citations
afffundunlabeled
Robust Linear Clustering
Luis Ángel García-Escudero, Alfonso Gordaliza, Stefan Van Aelst, Ruben H. Zamar
2008· article· en· Journal of the Royal Statistical Society Series B (Statistical Methodology)· Mathematics
machine prediction:candidate · noneconsensus · none
56
citations

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