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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.

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
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 7 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.

affvenueunlabeled
Marginally restricted sequential D‐optimal designs
Jesús López–Fidalgo, Raúl Martín Martín, Douglas P. Wiens
2008· article· en· Canadian Journal of Statistics· Decision Sciences
machine prediction:candidate · noneconsensus · none
1
citations
venueno affunlabeled
Extrapolation designs with constraints
Zhide Fang
2003· article· en· Canadian Journal of Statistics· Decision Sciences
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Design Effects
Neil Klar, Allan Donner
2014· other· en· Wiley StatsRef: Statistics Reference Online· Decision Sciences
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Experimental Planning
Gregory S. Patience, Ariane Bérard
2017· book-chapter· en· Elsevier eBooks· Decision Sciences
machine prediction:candidate · noneconsensus · none
1
citations
venueno affunlabeled
Optimization in 2 m 3 n Factorial Experiments
G. S. R. Murthy, D.K. Manna
2012· article· en· Algorithmic operations research· Decision Sciences
machine prediction:candidate · noneconsensus · none
1
citations
venueno affno abstractunlabeled
Partial Diallel Cross Block Designs.
Kuey Chung Choi, Sudhir Gupta, Young Nam Son
2002· article· en· Ars Combinatoria· Decision Sciences
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
A robust treatment of a dose–response study
Douglas P. Wiens, Pengfei Li
2011· article· en· Applied Stochastic Models in Business and Industry· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Beta approximation and its applications
Shu Ding, Xiaoping Shi, Yuehua Wu
2020· article· en· Journal of Statistical Computation and Simulation· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Job‐Exposure Matrices
Jack Siemiatycki
2014· other· en· Wiley StatsRef: Statistics Reference Online· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
afffundunlabeled
Robust designs for experiments with blocks
Rena K. Mann, Roderick Edwards, Julie Zhou
2016· preprint· en· arXiv (Cornell University)· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Experimental statistical design
Ammar Yahia, P.-C. Aı̈tcin
2015· book-chapter· en· Elsevier eBooks· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About