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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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Survey Sampling and Estimation Techniques
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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
fundfunder
venuejournal
aboutaboutness

The four routes compose: require the funder route and exclude affiliation to get the funder-only stratum no affiliation-based frame ever sees.

191 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.
191 works in the cohort · of 4,299,418page 2 of 4

Labels cover 0 of 191 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 191 of 191 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
A new design for sampling with adaptive sample plots
Haijun Yang, Christoph Kleinn, Lutz Fehrmann, Shouzheng Tang, Steen Magnussen
2009· article· en· Environmental and Ecological Statistics· Mathematics
machine prediction:candidate · noneconsensus · none
14
citations
affvenueaboutunlabeled
Variance estimation for two-phase stratified sampling
David A. Binder, Colin Babyak, Marie Brodeur, Michel Hidiroglou, Wisner Jocelyn
2000· article· en· Canadian Journal of Statistics· 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
aboutno affunlabeled
Calibration using power transformation
Veronica I. Salinas, Stephen A. Sedory, Sarjinder Singh
2018· article· en· Communications in Statistics - Simulation and Computation· Mathematics
machine prediction:candidate · noneconsensus · none
9
citations
affunlabeled
VARIANCE ESTIMATION IN TWO‐PHASE SAMPLING
M. Hidiroglou, J. N. K. Rao, David Haziza
2009· article· en· Australian & New Zealand Journal of Statistics· Mathematics
machine prediction:candidate · noneconsensus · none
6
citations
fundno affunlabeled
The one-sayers model for the Extended Crosswise design
Maarten Cruyff, Khadiga H. A. Sayed, Andrea Petróczi, P.G.M. van der Heijden
2024· article· en· Journal of the Royal Statistical Society Series A (Statistics in Society)· Mathematics
machine prediction:candidate · metaresearchconsensus · none
6
citations

How this was built: Screen · Findings · About