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

151 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.
151 works in the cohort · of 4,299,418page 3 of 4

Labels cover 0 of 151 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 151 of 151 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
Predicción de cobertura en sistemas LMDS/LMCS
Antonio Guillén Pérez, Luiz da Silva Mello, Jörg Sebastian Michaelis
2001· article· es· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Presentación
Miguel Sáenz Cardoza, Raquel Gutiérrez Estupiñán, Jaime Moreno Villarreal
2022· book-chapter· es· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
aboutno affunlabeled
EL MAGO VA AL FESTIVAL DE CINE DE MONTREAL
Jaime Blanco Aparicio
2004· article· es· Gaceta UNAM (2000-2009)· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Si quiere ganar puntos lea este artículo
Benoît Bréville, Pierre Rimbert
2013· article· es· Le Monde diplomatique en español· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Las cuentas económicas de la televisión
Karen Arriaza Ibarra
2008· article· es· Libros de economía y empresa· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Investigación de producción.
Carla Argañaraz
2002· article· es· Cuadernos del Centro de Estudios en Diseño y Comunicación. Ensayos· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Los ecos de las hachas
Justo Jorge Padrón
2000· article· en· Dialnet (Universidad de la Rioja)· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Formato, estilo y géneros televisivos
Jean-Claude Soulages
2005· article· es· Dipòsit Digital de Documents de la UAB (Universitat Autònoma de Barcelona)· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
RADIO UNAM GANA SEIS RECONOCIMIENTOS
Correa Raúl
2012· article· es· Gaceta UNAM (2010-2015)· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Los medios mexicanos y el TLCAN
Florence Toussaint Alcaráz
2012· book· es· Editorial Académica Española eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affno abstractunlabeled
Telefonía móvil: caracterización de las conexiones
Francisco Barceló-Arroyo, Javier Jordán Parra
2002· article· es· Dialnet (Universidad de la Rioja)· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About