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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.

3,084 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.
3,084 works in the cohort · of 4,299,418page 62 of 62

Labels cover 10 of 3,084 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 3,084 of 3,084 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
Multilingual Interface Usage.
Maria Gäde, Juliane Stiller
2011· article· fr· Ingénierie des systèmes d information· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Turker Script for Scenario 92.docx
David Topps, Michelle Cullen
2019· dataset· en· Harvard Dataverse· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
affunlabeled
Unsupervised Dependency Graph Network
2022· article· en· Greater South Information System· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
The Alaskan Athabascan Grammar Database
Sebastian Nordhoff, Siri G. Tuttle, Olga Lovick
2016· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Replication Package for SANER26
2025· other· en· Zenodo (CERN European Organization for Nuclear Research)· Computer Science
machine prediction:candidate · metaresearch+insufficient_payloadconsensus · none
0
citations
venueno affno abstractunlabeled
BBN's 2017 KBP EAL Submission.
Jay DeYoung, Yee Seng Chan, Chinnu Pittapally, Hannah Provenza, Ryan Gabbard, Marjorie Freedman
2017· article· en· Theory and applications of categories· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affunlabeled
Corpus_ngram3.tab
Andrew Piper, James Manalad
2019· dataset· fr· Harvard Dataverse· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
afffundunlabeled
THE HELL with questions
Michela Ippolito
2022· article· en· Journal of Semantics· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
afffundunlabeled
Analogy Training Multilingual Encoderss
Nicolas Garneau, lwp lwp, Anders Sandholm, Sebastian Ruder, Ivan Vulić, Anders Søgaard
2021· article· en· Research at the University of Copenhagen (University of Copenhagen)· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Infusing Syntax and Semantics into LLMs
Anton Bulle Labate, Fábio Gagliardi Cozman
2025· preprint· en· International Journal of Computational Intelligence Systems· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
stackr: vcf2dadi for ALF ;)
2016· other· en· Zenodo (CERN European Organization for Nuclear Research)· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affno abstractgemma · open_science+scholarly_communicationgpt · scholarly_communication+open_sciencemodels agree
Reflections on Remixing Open Access Content into Open Educational Resources: A New Paradigm for Sustainable Data-Driven Language Learning Systems Design in Higher Education
Alannah Fitzgerald, Shaoqun Wu, Jemma L König, Steven Shaw, Ian H. Witten
2023· book-chapter· en· Future education and learning spaces· Computer Science
machine prediction:candidate · open_scienceconsensus · none
0
citations

How this was built: Screen · Findings · About