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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.

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venuejournal
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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 50 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.

venueno affno abstractunlabeled
The University of Texas at Dallas HLTRI at TAC 2019.
Ramón Maldonado, Maxwell Weinzierl, Sanda M. Harabagiu
2019· article· en· Theory and applications of categories· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affunlabeled
DonnellyBJPSreplication.R
Michael Donnelly
2020· dataset· en· Harvard Dataverse· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
fundno affunlabeled
The Perfect Recipe: Add SUGAR, Add Data
Simone Magnolini, Vevake Balaraman, Marco Guerini, Bernardo Magnini
2018· book-chapter· en· Accademia University Press eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
afffundunlabeled
Abstractive Text Summarization Based on Neural Fusion
Yllias Chali, Wenzhao Zhu
2024· article· en· Proceedings of the ... International Florida Artificial Intelligence Research Society Conference· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
On-the-Fly Attention Modularization for Neural Generation
Yue Dong, Chandra Bhagavatula, Ximing Lu, Jena D. Hwang, Antoine Bosselut, Jackie Chi Kit Cheung +1 more
2021· preprint· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
venueno affunlabeled
CornPittMich Sentiment Slot-Filling System at TAC 2013
Carmen Banea, Rada Mihalcea, Yoonjung Choi, Lingjia Deng, Janyce Wiebe, Ozan İrsoy +2 more
2013· article· en· Theory and applications of categories· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Position Bias Across LLM Model Families
Salvatore Vella, Salah Sharieh, Alexander Ferworn
2025· article· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About