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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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Advanced Text Analysis Techniques
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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.

552 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.
552 works in the cohort · of 4,299,418page 2 of 12

Labels cover 2 of 552 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 552 of 552 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.

fundno affunlabeled
AckSeer
Madian Khabsa, Pucktada Treeratpituk, C. Lee Giles
2012· article· en· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
40
citations
affunlabeled
On the composition of scientific abstracts
Iana Atanassova, Marc Bertin, Vincent Larivière
2016· article· en· Journal of Documentation· Computer Science
machine prediction:candidate · metaresearch+bibliometricsconsensus · none
40
citations
affno abstractunlabeled
Chance Encounters in the Digital Library
Elaine G. Toms, Lori McCay‐Peet
2009· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
32
citations
affunlabeled
User-controlled link adaptation
Theophanis Tsandilas, m.c. schraefel
2003· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
30
citations
affunlabeled
Accuracy of Person-Fit Statistics
Christina St‐Onge, Pierre Valois, Belkacem Abdous, Stéphane Germain
2011· article· en· Applied Psychological Measurement· Computer Science
machine prediction:candidate · noneconsensus · none
28
citations
affunlabeled
Segmentation Similarity and Agreement
Chris Fournier, Diana Inkpen
2012· article· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
27
citations
affunlabeled
Text-Based Intelligent Learning Emotion System
Mohammed Abdel Razek, Claude Frasson
2017· article· en· Journal of Intelligent Learning Systems and Applications· Computer Science
machine prediction:candidate · noneconsensus · none
22
citations
affaboutunlabeled
Automation of Systematic Reviews with Large Language Models
Christian Cao, Rohit Arora, Paul Cento, Katherine Manta, Elina Farahani, Milena Cecere +18 more
2025· preprint· en· medRxiv· Computer Science
machine prediction:candidate · metaresearchconsensus · metaresearch
21
citations
afffundunlabeled
Improving interpretations of topic modeling in microblogs
Sarah A. Alkhodair, Benjamin C. M. Fung, Osmud Rahman, Patrick C. K. Hung
2017· article· en· Journal of the Association for Information Science and Technology· Computer Science
machine prediction:candidate · noneconsensus · none
20
citations
affunlabeled
Sentiment Analysis of StockTwits Using Transformer Models
Aysun Bozanta, Sabrina Angco, Mücahit Çevik, Ayşe Bener
2021· article· en· 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)· Computer Science
machine prediction:candidate · noneconsensus · none
19
citations

How this was built: Screen · Findings · About