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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 Causal Inference 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.

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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.

1,326 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.
1,326 works in the cohort · of 4,299,418page 1 of 27

Labels cover 35 of 1,326 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 1,326 of 1,326 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.

afffundno abstractgpt · no categorygrok · no categoryopus · no categorymodels agree
Regression discontinuity designs: A guide to practice
Guido W. Imbens, Thomas Lemieux
2007· article· en· Journal of Econometrics· Mathematics
machine prediction:candidate · noneconsensus · none
3,773
citations
afffundunlabeled
Reducing bias through directed acyclic graphs
Ian Shrier, Robert W. Platt
2008· article· en· BMC Medical Research Methodology· Mathematics
machine prediction:candidate · metaresearchconsensus · none
1,410
citations
affunlabeled
Illustrating bias due to conditioning on a collider
Stephen R. Cole, Robert W. Platt, Enrique F. Schisterman, Haitao Chu, Daniel Westreich, David J. Richardson +1 more
2009· article· en· International Journal of Epidemiology· Mathematics
machine prediction:candidate · metaresearchconsensus · none
823
citations
fundno affunlabeled
Experimental Designs for Identifying Causal Mechanisms
Kosuke Imai, Dustin Tingley, Teppei Yamamoto
2012· article· en· Journal of the Royal Statistical Society Series A (Statistics in Society)· Mathematics
machine prediction:candidate · metaresearchconsensus · none
407
citations
affno abstractunlabeled
Good Research Practices for Comparative Effectiveness Research: Analytic Methods to Improve Causal Inference from Nonrandomized Studies of Treatment Effects Using Secondary Data Sources: The ISPOR Good Research Practices for Retrospective Database Analysis Task Force Report—Part III
Michael L. Johnson, William H. Crown, Bradley C. Martin, Colin R. Dormuth, Uwe Siebert
2009· article· en· Value in Health· Mathematics
machine prediction:candidate · metaresearchconsensus · metaresearch
290
citations

How this was built: Screen · Findings · About