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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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Optimal Experimental Design Methods
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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.

442 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.
442 works in the cohort · of 4,299,418page 8 of 9

Labels cover 0 of 442 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 442 of 442 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
<scp>L</scp> atin Hypercube Designs
Boxin Tang
2014· other· en· Wiley StatsRef: Statistics Reference Online· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
venueno affunlabeled
Bayesian Inference and Posterior Simulators
John Geweke
2001· article· fr· Canadian Journal of Agricultural Economics/Revue canadienne d agroeconomie· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Experimental Design
Kim Koh
2014· book-chapter· en· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Response Rate
Alex C. Michalos, Alex C. Michalos
2014· book-chapter· en· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Effect Size Estimation in Multifactor Designs.
Rex B. Kline
2006· book-chapter· en· American Psychological Association eBooks· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
A journey of discovery with George Box
D. W. Bacon
2014· article· en· Applied Stochastic Models in Business and Industry· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Binary Response
Eric K. H. Chan
2023· book-chapter· en· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Optimal Approximate Design
Saumen Mandal
2025· book-chapter· en· International Encyclopedia of Statistical Science· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Experimental Design
Kim Koh
2023· book-chapter· en· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Factorial Design
David Nordstokke, S. Mitchell Colp
2023· book-chapter· en· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Sequential design for microarray experiments
Gilles Durrieu, Laurent Briollais
2005· preprint· en· HAL (Le Centre pour la Communication Scientifique Directe)· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Index
Ricardo A. Maronna, R. Douglas Martin, Vı́ctor J. Yohai, Matías Salibián‐Barrera
2018· paratext· en· Wiley series in probability and statistics· Decision Sciences
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affno abstractunlabeled
Special issue on Design of Experiments
A N Donev, Jesús López–Fidalgo, Douglas P. Wiens
2017· article· en· Computational Statistics & Data Analysis· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Nested Experimental Designs
Kirti R. Shah, Bikas K. Sinha
2014· other· en· Wiley StatsRef: Statistics Reference Online· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Optimal Regression Designs in Asymmetric Domains
Erkki P. Liski, Nripes Kumar Mandal, Kirti R. Shah, Bikas K. Sinha
2002· book-chapter· en· Lecture notes in statistics· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Experimental Design Using Interlacing Polynomials
Lap Chi Lau, Robert Wang, Hong Zhou
2025· book-chapter· en· Society for Industrial and Applied Mathematics eBooks· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Additional Selected Topics
Erkki P. Liski, Nripes Kumar Mandal, Kirti R. Shah, Bikas K. Sinha
2002· book-chapter· en· Lecture notes in statistics· Decision Sciences
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affno abstractunlabeled
Power Considerations in Designed Experiments
Luyao Lin, Derek Bingham, Ryan Lekivetz
2019· article· en· Journal of Statistical Theory and Practice· Decision Sciences
machine prediction:candidate · metaresearchconsensus · none
0
citations

How this was built: Screen · Findings · About