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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.

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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 10 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
Learning to Transfer Prompts for Text Generation
Junyi Li, Tianyi Tang, Jian‐Yun Nie, Ji-Rong Wen
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
28
citations
affno abstractunlabeled
Finding decision jumps in text classification
Xianggen Liu, Lili Mou, Haotian Cui, Zhengdong Lu, Sen Song
2019· article· en· Neurocomputing· Computer Science
machine prediction:candidate · noneconsensus · none
28
citations
venueno affunlabeled
Stanford's Distantly-Supervised Slot-Filling System
Mihai Surdeanu, Sonal Gupta, John Bauer, David McClosky, Anne Lynn S. Chang, Valentin I. Spitkovsky +1 more
2011· article· en· Theory and applications of categories· Computer Science
machine prediction:candidate · noneconsensus · none
28
citations
aboutno affunlabeled
A Replication Study of Dense Passage Retriever
Xueguang Ma, Kai Sun, Ronak Pradeep, Jimmy Lin
2021· preprint· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · metaresearchconsensus · none
27
citations
venueno affunlabeled
Automatic Dream Sentiment Analysis
David R. Nadeau, Catherine Sabourin, Joseph De Koninck, Stan Matwin, Peter D. Turney
2006· article· it· NPARC· Computer Science
machine prediction:candidate · noneconsensus · none
25
citations
affunlabeled
Extracting information networks from the blogosphere
Yuval Merhav, Filipe Mesquita, Denilson Barbosa, Wai Gen Yee, Ophir Frieder
2012· article· en· ACM Transactions on the Web· Computer Science
machine prediction:candidate · noneconsensus · none
25
citations
affno abstractunlabeled
Coherence-Based Automated Essay Scoring Using Self-attention
Xia Li, Minping Chen, Jian‐Yun Nie, Zhenxing Liu, Ziheng Feng, Yingdan Cai
2018· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
25
citations
affno abstractunlabeled
Argumentation Mining in Parliamentary Discourse
Nona Naderi, Graeme Hirst
2016· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
25
citations

How this was built: Screen · Findings · About