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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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Advanced Statistical Process Monitoring
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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
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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.

431 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.
431 works in the cohort · of 4,299,418page 6 of 9

Labels cover 1 of 431 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 431 of 431 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.

affno abstractunlabeled
Special Issue on Intelligent Healthcare Systems
Vijay Mago, Philippe J. Giabbanelli
2015· article· en· Journal of Intelligent Systems· Decision Sciences
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Empirical Likelihood Based Control Charts
Asokan Mulayath Variyath
2013· article· en· Economic Quality Control· Decision Sciences
machine prediction:candidate · noneconsensus · none
2
citations
venueno affunlabeled
Unit Roots in Time Series with Changepoints
Ed Herranz, James E. Gentle, George Wang
2017· article· en· International Journal of Statistics and Probability· Decision Sciences
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Discussion
Bovas Abraham, Jock MacKay
2001· article· en· Journal of Quality Technology· Decision Sciences
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Change acceleration and detection
Yanglei Song, Georgios Fellouris
2024· article· en· The Annals of Statistics· Decision Sciences
machine prediction:candidate · noneconsensus · none
1
citations
afffundunlabeled
Using Predictive Risk for Process Control
Jean‐François Plante, Gitte Bjørg Windfeldt
2012· article· en· Australian & New Zealand Journal of Statistics· Decision Sciences
machine prediction:candidate · noneconsensus · none
1
citations

How this was built: Screen · Findings · About