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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.

2,769 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.
2,769 works in the cohort · of 4,299,418page 53 of 56

Labels cover 6 of 2,769 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 2,769 of 2,769 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.

affunlabeled
The Quality of Discharge Summaries After AKI
C. Giles, Milica Novakovic, Wilma M. Hopman, Samuel A. Silver
2021· article· en· Journal of the American Society of Nephrology· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
simtax_old-checkpoint.py
Martin Boyer, Philippe D'Astous, Pierre‐Carl Michaud
2020· dataset· en· Harvard Dataverse· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Building an NLIDB: The Basics
Yunyao Li, Dragomir Radev, Davood Rafiei
2023· book-chapter· en· Synthesis lectures on data management· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Named Entity-based Question-Answering Pair Generator
Aritra Kumar Lahiri, Qinmin Hu
2022· article· en· Proceedings of the 31st ACM International Conference on Information & Knowledge Management· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Information Extraction
Professor Reda Alhajj, Professor Jon Rokne
2014· book-chapter· en· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Detecting Latent Signs
2025· book-chapter· en· Cambridge University Press eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
venueno affunlabeled
Domino: SAIC's English Entity-Linking System.
Alan Buabuchachart, Parakh Jain, Ryan O. Murphy, S. M. White, Leora Morgenstern
2012· article· en· Theory and applications of categories· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
fundno affunlabeled
Lightweight Latent Reasoning for Narrative Tasks
Alexander Gurung, Nikolay Malkin, Mirella Lapata
2025· preprint· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Beyond term clusters
Ozge Yeloglu, Evangelos Milios, A. Nur Zincir‐Heywood
2013· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
venueno affno abstractunlabeled
JHU/APL CONSENSE: The Confidence-Based System Ensembler.
Michael D. Lieberman, Christine Piatko, I-Jeng Wang, Joseph Downs, P. Wilson
2015· article· en· Theory and applications of categories· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About