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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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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.

183 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.
183 works in the cohort · of 4,299,418page 3 of 4

Labels cover 1 of 183 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 183 of 183 works in this cohort. Predictions are machine_predicted_unvalidated teacher distillation outputs. Candidate is the union; consensus is the intersection.

affno abstractunlabeled
Runs in continuous-valued sequences
Serkan Eryılmaz, James C. Fu
2007· article· en· Statistics & Probability Letters· Decision Sciences
distilled prediction:candidate · metaresearchconsensus · none
4
citations
affno abstractunlabeled
A relative-rank measure for the rank transformation
Abid Hussain, Steve Drekic, Salman Cheema
2023· article· en· Statistics & Probability Letters· Decision Sciences
distilled prediction:candidate · metaresearchconsensus · none
3
citations
afffundno abstractunlabeled
Three skewed matrix variate distributions
Michael P. B. Gallaugher, Paul D. McNicholas
2018· preprint· en· Statistics & Probability Letters· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
2
citations
afffundno abstractunlabeled
M-Vine decomposition and VAR(1) models
Étienne Bégin, Pierre Dutilleul, Carole Beaulieu, Taoufik Bouezmarni
2019· article· en· Statistics & Probability Letters· Agricultural and Biological Sciences
distilled prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Is variance larger if and only if tails are larger?
Michael D. deB. Edwardes
2000· article· en· Statistics & Probability Letters· Mathematics
distilled prediction:candidate · metaepi_narrow+insufficient_payloadconsensus · none
2
citations
affno abstractunlabeled
A new class of nearly self-financing strategies
Donna Salopek
2002· article· en· Statistics & Probability Letters· Economics, Econometrics and Finance
distilled prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
A converse to precise asymptotic results
Deli Li, Aurel Spătaru
2005· article· en· Statistics & Probability Letters· Decision Sciences
distilled prediction:candidate · metaresearch+metaepi_narrow+insufficient_payloadconsensus · none
1
citations
affunlabeled
On absolute moment-based upper bounds for L-moments
M. C. Jones, N. Balakrishnan
2024· article· en· Statistics & Probability Letters· Economics, Econometrics and Finance
distilled prediction:candidate · metaepi_narrowconsensus · none
1
citations
affno abstractunlabeled
Simulating tail asymptotics of a Markov chain
Aziz Khanchi, Gilles Lamothe
2011· article· en· Statistics & Probability Letters· Mathematics
distilled prediction:candidate · metaepi_narrowconsensus · none
1
citations
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