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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.

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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 3 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.

afffundunlabeled
Optimal split‐plot orthogonal arrays
Po Yang, Chang‐Yun Lin
2017· article· en· Australian & New Zealand Journal of Statistics· Decision Sciences
machine prediction:candidate · noneconsensus · none
14
citations
affno abstractunlabeled
Constructing optimal designs with constraints
Saumen Mandal, B. Torsney, Keumhee C. Carrière
2004· article· en· Journal of Statistical Planning and Inference· Decision Sciences
machine prediction:candidate · noneconsensus · none
14
citations
afffundvenueaboutunlabeled
Design selection for strong orthogonal arrays
Chenlu Shi, Boxin Tang
2019· article· en· Canadian Journal of Statistics· Decision Sciences
machine prediction:candidate · noneconsensus · none
13
citations
afffundunlabeled
Generalized Functional Extended Redundancy Analysis
Heungsun Hwang, Hye Won Suk, Yoshio Takane, Jang-Han Lee, Jooseop Lim
2013· article· en· Psychometrika· Decision Sciences
machine prediction:candidate · noneconsensus · none
12
citations
afffundno abstractunlabeled
Folded over non-orthogonal designs
Cong Lin, Arden Miller, R. R. Sitter
2007· article· en· Journal of Statistical Planning and Inference· Decision Sciences
machine prediction:candidate · noneconsensus · none
12
citations
afffundno abstractunlabeled
On the orthogonal designs of order 40
W. H. Holzmann, Hadi Kharaghani
2001· article· en· Journal of Statistical Planning and Inference· Decision Sciences
machine prediction:candidate · metaresearchconsensus · none
10
citations
affno abstractunlabeled
On the orthogonal designs of order 24
W. H. Holzmann, Hadi Kharaghani
2000· article· en· Discrete Applied Mathematics· Decision Sciences
machine prediction:candidate · noneconsensus · none
9
citations
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