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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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Blockchain Technology Applications and Security
Retraction
Abstract
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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
fundfunder
venuejournal
aboutaboutness

The four routes compose: require the funder route and exclude affiliation to get the funder-only stratum no affiliation-based frame ever sees.

2,298 results · 1 filter active ·
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20002025
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Machine labels · sparse coverage
Evidence
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
2,298 works in the cohort · of 4,299,418page 43 of 46

Labels cover 6 of 2,298 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 2,298 of 2,298 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
Buyer-Optimal Algorithmic Consumption
Shota Ichihashi, Alex Smolin
2023· article· en· SSRN Electronic Journal· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Monitoring and Enforcing Online Auction Ethics
Diana Kao, Shouhong Wang
2007· book-chapter· en· Advances in intelligent information technologies series/Advances in intelligent information technologies (AIIT) book series· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affno abstractunlabeled
Canada
2019· article· en· Project Muse (Johns Hopkins University)· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Cryptocurrency Shocks
Jinan Liu, Sajjadur Rahman, Apostolos Serletis
2020· article· en· SSRN Electronic Journal· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Private Settlement in Blockchain Systems
Motahhareh Moravvej-Hamedani, Alfred Lehar
2022· article· en· SSRN Electronic Journal· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affvenueaboutunlabeled
Alexa, Please: Babysit My Child
Kayla Clarke
2021· article· en· Stream Interdisciplinary Journal of Communication· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Irving Fisher, Ronald Coase, and DeFi
Adam Aldad, Frank T. Lorne
2022· article· en· International Journal of Electronic Banking· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Améliorer le Bitcoin ... à coup de fourches
Emmanuelle Anceaume, Jean-Michel Prima
2018· preprint· fr· HAL (Le Centre pour la Communication Scientifique Directe)· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Cryptocurrency and blockchain explained
Michael Graham, Igor Dosen
2018· article· en· Analysis & Policy Observatory· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About