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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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Explainable Artificial Intelligence (XAI)
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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
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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.

675 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.
675 works in the cohort · of 4,299,418page 2 of 14

Labels cover 1 of 675 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 675 of 675 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.

affunlabeled
Deep Learning: Foundations and Concepts
Blanka Horvath, Anastasis Kratsios, Raeid Saqur
2024· article· en· Quantitative Finance· Computer Science
machine prediction:candidate · noneconsensus · none
56
citations
affunlabeled
The Role of Interactive Visualization in Fostering Trust in AI
Emma Beauxis-Aussalet, Michael Behrisch, Rita Borgo, Duen Horng Chau, Christopher Collins, David S. Ebert +10 more
2021· article· en· IEEE Computer Graphics and Applications· Computer Science
machine prediction:candidate · noneconsensus · none
40
citations
affunlabeled
Who Determines What Is Relevant? Humans or AI? Why Not Both?
Guglielmo Faggioli, Laura Dietz, Charles L. A. Clarke, Gianluca Demartini, Matthias Hagen, Claudia Hauff +5 more
2024· article· en· Communications of the ACM· Computer Science
machine prediction:candidate · noneconsensus · none
40
citations
affunlabeled
Artificial Intelligence
David Poole, Alan K. Mackworth
2023· book· en· Cambridge University Press eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
38
citations
affunlabeled
Cracking Open the Black Box
Arnaud Dethise, Marco Canini, Srikanth Kandula
2019· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
38
citations
fundno affunlabeled
Human-AI collaboration to identify literature for evidence synthesis
Scott Spillias, P Tuohy, Matthew Andreotta, R Annand-Jones, Fabio Boschetti, Christopher Cvitanovic +6 more
2024· article· en· Cell Reports Sustainability· Computer Science
machine prediction:candidate · metaresearchconsensus · metaresearch
34
citations
affunlabeled
xxAI - Beyond Explainable Artificial Intelligence
Andreas Holzinger, Randy Goebel, Ruth Fong, Taesup Moon, Klaus‐Robert Müller, Wojciech Samek
2022· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
33
citations
affunlabeled
Interpretable Random Forests via Rule Extraction
Clément Bénard, Gérard Biau, Sébastien da Veiga, Erwan Scornet
2020· preprint· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
29
citations

How this was built: Screen · Findings · About