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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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Machine Learning and Knowledge Extraction
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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.

51 results · 1 filter active ·
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20172025
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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.
51 works in the cohort · of 4,299,418page 1 of 2

Labels cover 0 of 51 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 51 of 51 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
Fairness and Explanation in AI-Informed Decision Making
Alessa Angerschmid, Jianlong Zhou, Kevin Theuermann, Fang Chen, Andreas Holzinger
2022· article· en· Machine Learning and Knowledge Extraction· Social Sciences
machine prediction:candidate · noneconsensus · none
148
citations
affunlabeled
Node-Centric Pruning: A Novel Graph Reduction Approach
Hossein Shokouhinejad, Roozbeh Razavi‐Far, Griffin Higgins, Ali A. Ghorbani
2024· article· en· Machine Learning and Knowledge Extraction· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
afffundunlabeled
Locally-Scaled Kernels and Confidence Voting
Elizabeth Hofer, Martin v. Mohrenschildt
2024· article· en· Machine Learning and Knowledge Extraction· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
afffundunlabeled
CRISP-NET: Integration of the CRISP-DM Model with Network Analysis
Héctor Alejandro Acuña-Cid, Eduardo Ahumada‐Tello, Óscar Omar Ovalle Osuna, Richard Evans, Julia Elena Hernández-Ríos, Miriam Alondra Zambrano-Soto
2025· article· en· Machine Learning and Knowledge Extraction· Physics and Astronomy
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About