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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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Data Analysis with R
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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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venuejournal
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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.

814 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.
814 works in the cohort · of 4,299,418page 2 of 17

Labels cover 3 of 814 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 814 of 814 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
Programming with R
W. John Braun, Duncan J. Murdoch
2007· book-chapter· en· Cambridge University Press eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
8
citations
affunlabeled
Fun with the R Grid Package
Lutong Zhou, W. John Braun
2010· article· en· Journal of Statistics Education· Computer Science
machine prediction:candidate · noneconsensus · none
7
citations
affunlabeled
SimPHO: an ontology for simulation modeling of population health
Anya Okhmatovskaia, David L. Buckeridge, Arash Shaban‐Nejad, Andrew Sutcliffe, Philippe Finès, Jacek A. Kopec +1 more
2012· article· en· Winter Simulation Conference· Computer Science
machine prediction:candidate · noneconsensus · none
7
citations
affno abstractunlabeled
Advanced Statistical Methods in Data Science
Hao Yu, Grace Y. Yi, Xuewen Lu, Jiahua Chen, Ding‐Geng Chen
2016· book· en· ICSA book series in statistics· Computer Science
machine prediction:candidate · noneconsensus · none
5
citations
affunlabeled
Transparency in empirical economic research
Cristina Blanco-Perez, Abel Brodeur
2019· article· en· IZA World of Labor· Computer Science
machine prediction:candidate · metaresearchconsensus · metaresearch
3
citations
affunlabeled
A Practical Guide to Data Analysis Using R
John H. Maindonald, W. John Braun, Jeffrey L. Andrews
2024· book· en· Cambridge University Press eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
The History of ViSta: The Visual Statistics System
Pedro Valero‐Mora, Rubén Daniel Ledesma, Michael Friendly
2012· review· en· Wiley Interdisciplinary Reviews Computational Statistics· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
aboutno affunlabeled
rdataretriever: R Interface to the Data Retriever
Henry Senyondo, Daniel J. McGlinn, Pranita Sharma, David J. Harris, Hao Ye, Shawn D. Taylor +7 more
2017· dataset· en· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affvenueunlabeled
Research Methods: Altmetrics
Virginia Wilson
2016· article· en· Evidence Based Library and Information Practice· Computer Science
machine prediction:candidate · metaresearch+bibliometrics+insufficient_payloadconsensus · none
2
citations
affunlabeled
A brief introduction to R
John H. Maindonald, W. John Braun
2013· book-chapter· en· Cambridge University Press eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations

How this was built: Screen · Findings · About