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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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Journal of the Royal Statistical Society Series B (Statistical Methodology)
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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.

82 results · 1 filter active ·
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20002024
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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.
82 works in the cohort · of 4,299,418page 2 of 2

Labels cover 0 of 82 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 82 of 82 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
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
affunlabeled
Nonparametric Density Estimation Over Complicated Domains
Federico Ferraccioli, Eleonora Arnone, Livio Finos, J. O. Ramsay, Laura M. Sangalli
2021· article· en· Journal of the Royal Statistical Society Series B (Statistical Methodology)· Environmental Science
machine prediction:candidate · noneconsensus · none
15
citations
aboutno affunlabeled
Adaptive Varying-Coefficient Linear Models
Jianqing Fan, Qiwei Yao, Zongwu Cai
2003· article· en· Journal of the Royal Statistical Society Series B (Statistical Methodology)· Chemistry
machine prediction:candidate · noneconsensus · none
14
citations
afffundunlabeled
Statistical Classification with Missing Covariates
Majid Mojirsheibani, Zahra Montazeri
2007· article· en· Journal of the Royal Statistical Society Series B (Statistical Methodology)· Mathematics
machine prediction:candidate · noneconsensus · none
13
citations
affunlabeled
Inference for Two-Stage Sampling Designs
Guillaume Chauvet, Audrey‐Anne Vallée
2020· article· en· Journal of the Royal Statistical Society Series B (Statistical Methodology)· Mathematics
machine prediction:candidate · noneconsensus · none
12
citations
affunlabeled
Causal inference on distribution functions
Zhenhua Lin, Dehan Kong, Linbo Wang
2023· article· en· Journal of the Royal Statistical Society Series B (Statistical Methodology)· Mathematics
machine prediction:candidate · noneconsensus · none
11
citations
aboutno affunlabeled
Report of the Editors—2003
A. C. Davison, R. Henderson
2003· article· en· Journal of the Royal Statistical Society Series B (Statistical Methodology)· Decision Sciences
machine prediction:candidate · noneconsensus · none
9
citations
affunlabeled
Modified Likelihood root in High Dimensions
Yanbo Tang, Nancy Reid
2020· article· en· Journal of the Royal Statistical Society Series B (Statistical Methodology)· Mathematics
machine prediction:candidate · noneconsensus · none
9
citations
affunlabeled
On inference in high-dimensional regression
Heather Battey, Nancy Reid
2023· article· en· Journal of the Royal Statistical Society Series B (Statistical Methodology)· Mathematics
machine prediction:candidate · noneconsensus · none
5
citations
affunlabeled
Moment Conditions and Bayesian Non-Parametrics
Luke Bornn, Neil Shephard, Reza Solgi
2018· preprint· en· Journal of the Royal Statistical Society Series B (Statistical Methodology)· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affunlabeled
Discussion on the paper by Brooks, Giudici and Roberts
Christian P. Robert, Xiao‐Li Meng, Jesper Møller, Jeffrey S. Rosenthal, Christopher Jennison, M. A. Hurn +18 more
2003· article· en· Journal of the Royal Statistical Society Series B (Statistical Methodology)· Mathematics
machine prediction:candidate · noneconsensus · none
3
citations

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