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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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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.

867 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.
867 works in the cohort · of 4,299,418page 5 of 18

Labels cover 10 of 867 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 867 of 867 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.

afffundunlabeled
Data Quality Challenges in Twitter Content Analysis for Informing Policy Making in Health Care
Axel J. Soto, Cynthia Ryan, Fernando Peña Silva, Tapajyoti Das, Jacek Wołkowicz, Evangelos Milios +1 more
2018· article· en· Proceedings of the ... Annual Hawaii International Conference on System Sciences/Proceedings of the Annual Hawaii International Conference on System Sciences· Decision Sciences
machine prediction:candidate · metaresearchconsensus · none
11
citations
affno abstractunlabeled
Environmental Decision Support Systems: Exactly What Are They?
David Swayne, Ralf Denzer, Linda Lilburne, M. Purvis, Nigel W.T. Quinn, AM Storey
2000· book-chapter· en· IFIP advances in information and communication technology· Decision Sciences
machine prediction:candidate · noneconsensus · none
11
citations
affunlabeled
A FOUNDATION FOR OPEN INFORMATION ENVIRONMENTS
Jeffrey Parsons, Yair Wand
2014· article· en· Journal of the Association for Information Systems· Decision Sciences
machine prediction:candidate · open_scienceconsensus · none
10
citations
affunlabeled
Discovering Conservation Rules
Lukasz Golab, Howard Karloff, Flip Korn, Barna Saha, Divesh Srivastava
2012· article· en· Decision Sciences
machine prediction:candidate · noneconsensus · none
10
citations
affunlabeled
Storage model for CDA documents
Peter Bodorik, Michael Shepherd
2003· article· en· Decision Sciences
machine prediction:candidate · noneconsensus · none
9
citations
venueno affunlabeled
A Fuzzy-Match Search Engine for Physician Directories
Majid Rastegar-Mojarad, Christopher Kadolph, Zhan Ye, Daniel Wall, Narayana Murali, Simon Lin
2014· article· en· JMIR Medical Informatics· Decision Sciences
machine prediction:candidate · noneconsensus · none
9
citations

How this was built: Screen · Findings · About