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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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Markov Chains and Monte Carlo 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
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.

418 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.
418 works in the cohort · of 4,299,418page 2 of 9

Labels cover 1 of 418 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 418 of 418 works in this cohort. Predictions are machine_predicted_unvalidated teacher distillation outputs. Candidate is the union; consensus is the intersection.

affvenueunlabeled
On cross‐validation of Bayesian models
Fatemah Alqallaf, Paul Gustafson
2001· article· en· Canadian Journal of Statistics· Mathematics
distilled prediction:candidate · noneconsensus · none
30
citations
affunlabeled
Hidden Markov Models
John van der Hoek, Robert J. Elliott
2018· book-chapter· en· Cambridge University Press eBooks· Mathematics
distilled prediction:candidate · metaepi_narrowconsensus · none
29
citations
affunlabeled
The polar slice sampler
Gareth O. Roberts, Jeffrey S. Rosenthal
2002· article· en· Stochastic Models· Mathematics
distilled prediction:candidate · noneconsensus · none
28
citations
affunlabeled
Pipage Rounding, Pessimistic Estimators and Matrix Concentration
Neil Olver
2014· article· en· London School of Economics and Political Science Research Online (London School of Economics and Political Science)· Mathematics
distilled prediction:candidate · metaresearch+metaepi_narrow+stsconsensus · none
25
citations
affno abstractunlabeled
Scaling Analysis of Delayed Rejection MCMC Methods
Mylène Bédard, Randal Douc, Éric Moulines
2013· article· en· Methodology And Computing In Applied Probability· Mathematics
distilled prediction:candidate · noneconsensus · none
24
citations
affunlabeled
Perfect Forward Simulation via Simulated Tempering
Stephen P. Brooks, Yanan Fan, Jeffrey S. Rosenthal
2006· article· en· Communications in Statistics - Simulation and Computation· Mathematics
distilled prediction:candidate · noneconsensus · none
22
citations
affno abstractunlabeled
A Note on Eigenvalues of Liouvilleans
Vojkan Jakšić, Claude‐Alain Pillet
2001· article· en· Journal of Statistical Physics· Mathematics
distilled prediction:candidate · noneconsensus · none
19
citations
fundno affunlabeled
Navigator function for the conformal bootstrap
Marten Reehorst, Slava Rychkov, David Simmons–Duffin, Benoit Sirois, Ning Su, Balt C. van Rees
2021· preprint· en· SciPost Physics· Mathematics
distilled prediction:candidate · metaepi_narrowconsensus · none
18
citations
affunlabeled
Weight-preserving simulated tempering
Nicholas G. Tawn, Gareth O. Roberts, Jeffrey S. Rosenthal
2019· article· en· Statistics and Computing· Mathematics
distilled prediction:candidate · noneconsensus · none
17
citations
afffundno abstractunlabeled
Efficient use of exact samples
Duncan J. Murdoch, Jeffrey S. Rosenthal
2000· article· en· Statistics and Computing· Mathematics
distilled prediction:candidate · noneconsensus · none
16
citations

How this was built: Screen · Findings · About