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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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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.

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.

442 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.
442 works in the cohort · of 4,299,418page 2 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
[no title]
Nha Vo‐Thanh, Raf Jans, Eric D. Schoen, Peter Goos
2018· article· en· Anet (University of Antwerp)· Decision Sciences
machine prediction:candidate · insufficient_payloadconsensus · none
22
citations
affno abstractunlabeled
Applying the permutation test to factorial designs
D. J. K. Mewhort, Brendan T. Johns, Matthew A. Kelly
2010· article· en· Behavior Research Methods· Decision Sciences
machine prediction:candidate · noneconsensus · none
22
citations
affno abstractunlabeled
Two-level factorial experiments
Byran J. Smucker, Martin Krzywinski, Naomi Altman
2019· article· en· Nature Methods· Decision Sciences
machine prediction:candidate · noneconsensus · none
21
citations
venueno affunlabeled
Optimal design for the proportional odds model
Inna Perevozskaya, William F. Rosenberger, Linda M. Haines
2003· article· en· Canadian Journal of Statistics· Decision Sciences
machine prediction:candidate · noneconsensus · none
19
citations
afffundunlabeled
Robustness of Design in Dose–Response Studies
Pengfei Li, Douglas P. Wiens
2011· article· en· Journal of the Royal Statistical Society Series B (Statistical Methodology)· Decision Sciences
machine prediction:candidate · metaresearchconsensus · metaresearch
19
citations
affno abstractunlabeled
Robust model-based sampling designs
A. H. Welsh, Douglas P. Wiens
2012· article· en· Statistics and Computing· Decision Sciences
machine prediction:candidate · noneconsensus · none
19
citations
affno abstractunlabeled
Robust designs for misspecified logistic models
Adeniyi J. Adewale, Douglas P. Wiens
2008· article· en· Journal of Statistical Planning and Inference· Decision Sciences
machine prediction:candidate · noneconsensus · none
18
citations
affvenueunlabeled
for misspecified regression models
Peilin Shi, Jane J. Ye, Julie Zhou
2003· article· en· Canadian Journal of Statistics· Decision Sciences
machine prediction:candidate · noneconsensus · none
16
citations
affaboutunlabeled
Eliminating systematic bias from case-crossover designs
Xiaoming Wang, Sukun Wang, Warren B. Kindzierski
2018· article· en· Statistical Methods in Medical Research· Decision Sciences
machine prediction:candidate · metaresearchconsensus · metaresearch
16
citations
affno abstractunlabeled
De-aliasing effects using semifoldover techniques
N. Balakrishnan, Po Yang
2009· article· en· Journal of Statistical Planning and Inference· Decision Sciences
machine prediction:candidate · noneconsensus · none
16
citations
affno abstractunlabeled
Robustness of A-optimal designs
Joe Masaro, Chi Song Wong
2008· article· en· Linear Algebra and its Applications· Decision Sciences
machine prediction:candidate · noneconsensus · none
16
citations
affno abstractunlabeled
Constructing non-regular robust parameter designs
Jason L. Loeppky, Derek Bingham, R. R. Sitter
2005· article· en· Journal of Statistical Planning and Inference· Decision Sciences
machine prediction:candidate · noneconsensus · none
15
citations
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