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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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Revue d intelligence artificielle
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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.

717 results · 1 filter active ·
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20012025
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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.
717 works in the cohort · of 4,299,418page 8 of 15

Labels cover 2 of 717 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 717 of 717 works in this cohort. Predictions are machine_predicted_unvalidated teacher distillation outputs. Candidate is the union; consensus is the intersection.

venueno affunlabeled
Analysis of the MapReduce Performance in Hadoop
Nour-Eddine Bakni, Ismail Assayad
2024· article· fr· Revue d intelligence artificielle· Computer Science
distilled prediction:candidate · noneconsensus · none
2
citations
venueno affno abstractunlabeled
Processus, événements et couplages temporels et causaux
Gilles Kassel
2017· article· fr· Revue d intelligence artificielle· Arts and Humanities
distilled prediction:candidate · metaepi_narrow+sts+scholarly_communication+insufficient_payloadconsensus · insufficient_payload
2
citations
venueno affunlabeled
Assessment of Cardiovascular Disease Using Machine Learning
Divya Adusumilli, Sree Lakshmi Damineni, K. Swathi, Nagamani Tenali, Ramu Yadavalli
2024· article· fr· Revue d intelligence artificielle· Health Professions
distilled prediction:candidate · metaepi_narrow+research_integrity+insufficient_payloadconsensus · insufficient_payload
2
citations
venueno affunlabeled
Réseaux GAI pour la prise de décision
Christophe Gonzales, Patrice Perny, Sergio Queiroz
2007· article· fr· Revue d intelligence artificielle· Computer Science
distilled prediction:candidate · metaepi_narrow+insufficient_payloadconsensus · none
2
citations
venueno affunlabeled
L'analogie entre catégorisation et expression
Michèle Prandi
2003· article· fr· Revue d intelligence artificielle· Arts and Humanities
distilled prediction:candidate · metaepi_narrow+insufficient_payloadconsensus · insufficient_payload
2
citations
venueno affunlabeled
Android Malware Classification Using LSTM Model
Nagababu Pachhala, S. Jothilakshmi, Bhanu Prakash Battula
2022· article· en· Revue d intelligence artificielle· Computer Science
distilled prediction:candidate · noneconsensus · none
2
citations
venueno affunlabeled
SMILK, trait d’union entre langue naturelle et données sur le web
Cédric Lopez, Molka Tounsi Dhouib, Elena Cabrio, Catherine Faron Zucker, Fabien Gandon
2018· article· fr· Revue d intelligence artificielle· Computer Science
distilled prediction:candidate · metaepi_narrow+insufficient_payloadconsensus · insufficient_payload
2
citations

How this was built: Screen · Findings · About