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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
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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 34 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.

aboutno affunlabeled
Plain Language Summaries
Richard G. Langley, Karin Reich, S Darn, S. Leech, Andrew J. Taylor
2014· article· en· British Journal of Dermatology· Computer Science
machine prediction:candidate · metaresearch+insufficient_payloadconsensus · none
2
citations
affunlabeled
Filtering or adapting
Lixin Shi, Jian‐Yun Nie
2006· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
What a Creole Wants, What a Creole Needs
Heather Lent, Kelechi Ogueji, Miryam de Lhoneux, Orevaoghene Ahia, Anders Søgaard
2022· preprint· en· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
9. Usage-based Grammar
Ritva Laury, Tsuyoshi Ono
2019· book-chapter· en· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
afffundunlabeled
Sometimes We Want Ungrammatical Translations
Prasanna Parthasarathi, Koustuv Sinha, Joëlle Pineau, Adina Williams
2021· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
afffundunlabeled
Word sense extension
Lei Yu, Yang Xu
2023· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Restrictions on ordering of adjectives in Spanish
Ana Teresa Pérez‐Leroux, Alexander Tough, Erin Pettibone, Crystal Chen
2020· article· en· Borealis – An International Journal of Hispanic Linguistics· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affaboutunlabeled
Plum2Text
Nicolas Garneau, Eve Gaumond, Luc Lamontagne, Pierre-Luc Déziel
2021· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations

How this was built: Screen · Findings · About