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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
venuejournal
aboutaboutness

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 10 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 teacher distillation outputs. Candidate is the union; consensus is the intersection.

affunlabeled
Learning and Reasoning with Uncertainty
2023· book-chapter· en· Cambridge University Press eBooks· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
0
citations
affunlabeled
Designing and Personalising Hybrid Health Explanations for Lay Users
Maxwell Szymanski, Stijn Keyaerts, Cristina Conati, Robin De Croon, Vero Vanden Abeele, Katrien Verbert
2025· article· en· ACM Transactions on Interactive Intelligent Systems· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
0
citations
fundvenueaboutno affunlabeled
Reasonable Apprehension of AI Bias
Teresa Scassa
2024· article· en· McGill Law Journal· Computer Science
distilled prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Explainability of Algorithms
Andrés Páez
2025· preprint· en· Computer Science
distilled prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Retrospect and Prospect
2023· book-chapter· en· Cambridge University Press eBooks· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
0
citations
aboutno affunlabeled
Tanytarsus miriforceps
2025· article· en· Zenodo (CERN European Organization for Nuclear Research)· Computer Science
distilled prediction:candidate · sts+scholarly_communication+insufficient_payloadconsensus · insufficient_payload
0
citations
affunlabeled
Editorial: Explanation in human-AI systems
Anastassia Angelopoulou, Epaminondas Kapetanios, David Harris Smith, Volker Steuber, Bencie Woll, Frauke Zeller
2022· editorial· en· Frontiers in Artificial Intelligence· Computer Science
distilled prediction:candidate · metaepi_narrow+scholarly_communication+research_integrityconsensus · research_integrity
0
citations
aboutno affunlabeled
Anisodactylus (Anisodactylus) californicus Dejean 1829
2025· article· en· Zenodo (CERN European Organization for Nuclear Research)· Computer Science
distilled prediction:candidate · sts+scholarly_communication+insufficient_payloadconsensus · insufficient_payload
0
citations
affunlabeled
Supervised Machine Learning
2023· book-chapter· en· Cambridge University Press eBooks· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
0
citations
afffundunlabeled
Evaluating the Effects of AI Directors for Quest Selection
Kristen K. Yu, Matthew Guzdial, Nathan Sturtevant
2024· article· en· Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment· Computer Science
distilled prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Norway's Fair Share of Meeting the Paris Agreement
2018· report· en· Zenodo (CERN European Organization for Nuclear Research)· Computer Science
distilled prediction:candidate · sts+scholarly_communication+insufficient_payloadconsensus · insufficient_payload
0
citations
affno abstractunlabeled
Will User-Contributed AI Training Data Eat Its Own Tail?
Joshua S. Gans
2024· preprint· en· SSRN Electronic Journal· Computer Science
distilled prediction:candidate · metaepi_narrow+scholarly_communication+open_science+research_integrityconsensus · none
0
citations
affunlabeled
Can Ensembling Pre-processing Algorithms Lead to Better Machine Learning Fairness?
Khaled Badran, Pierre-Olivier Côté, Amanda Kolopanis, Rached Bouchoucha, Antonio Collante, Diego Elias Costa +2 more
2022· article· en· Zenodo (CERN European Organization for Nuclear Research)· Computer Science
distilled prediction:candidate · sts+scholarly_communication+insufficient_payloadconsensus · insufficient_payload
0
citations
affno abstractunlabeled
The Explainability Imperative
Michael Ridley
2024· article· en· SSRN Electronic Journal· Computer Science
distilled prediction:candidate · scholarly_communicationconsensus · none
0
citations

How this was built: Screen · Findings · About