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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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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
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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.

2,769 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.
2,769 works in the cohort · of 4,299,418page 14 of 56

Labels cover 6 of 2,769 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 2,769 of 2,769 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
Focused Hierarchical RNNs for Conditional Sequence Processing
Nan Rosemary Ke, Konrad Żołna, Alessandro Sordoni, Zhouhan Lin, Adam Trischler, Yoshua Bengio +3 more
2018· article· en· PolyPublie (École Polytechnique de Montréal)· Computer Science
machine prediction:candidate · noneconsensus · none
16
citations
affunlabeled
Distributed Negative Sampling for Word Embeddings
Stergios Stergiou, Zygimantas Straznickas, Rolina Wu, Kostas Tsioutsiouliklis
2017· article· en· Proceedings of the AAAI Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
16
citations
afffundunlabeled
Aligning Language Models to User Opinions
EunJeong Hwang, Bodhisattwa Prasad Majumder, Niket Tandon
2023· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
16
citations
fundno affunlabeled
How Gender Debiasing Affects Internal Model Representations, and Why It Matters
Hadas Orgad, Seraphina Goldfarb-Tarrant, Yonatan Belinkov
2022· article· en· Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies· Computer Science
machine prediction:candidate · noneconsensus · none
15
citations
affunlabeled
Ordered Memory
Yikang Shen, Shawn Tan, Arian Hosseini, Zhouhan Lin, Alessandro Sordoni, Aaron Courville
2019· article· en· Neural Information Processing Systems· Computer Science
machine prediction:candidate · noneconsensus · none
15
citations
affunlabeled
Overview of the TREC 2011 Legal Track.
Maura R. Grossman, Gordon V. Cormack, Bruce Hedin, Douglas W. Oard
2011· article· en· Text REtrieval Conference· Computer Science
machine prediction:candidate · noneconsensus · none
15
citations

How this was built: Screen · Findings · About