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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.

923 results · 1 filter active ·
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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.
923 works in the cohort · of 4,299,418page 7 of 19

Labels cover 923 of 923 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 923 of 923 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.

aboutno affgemma · bibliometricsgpt · bibliometricsmodels split
Leading countries in computer science: A bibliometric overview
Gustavo Zurita, José M. Merigó, Valeria Lobos-Ossandón, Carles Mulet-Forteza
2020· article· en· Journal of Intelligent & Fuzzy Systems· Computer Science
machine prediction:candidate · bibliometricsconsensus · none
8
citations
affgemma · metaresearch+bibliometricsgpt · bibliometrics+metaresearchmodels split
Diversity in the medical research ecosystem: a descriptive scientometric analysis of over 49 000 studies and 150 000 authors published in high-impact medical journals between 2007 and 2022
Marie‐Laure Charpignon, João Matos, Luis Filipe Nakayama, Jack Gallifant, Pia Gabrielle I. Alfonso, Marisa Cobanaj +9 more
2025· article· en· BMJ Open· Social Sciences
machine prediction:candidate · metaresearch+bibliometricsconsensus · none
8
citations
affgemma · no categorygpt · bibliometricsmodels split
A new approach to web co‐link analysis
Liwen Vaughan, Anton Ninkov
2018· article· en· Journal of the Association for Information Science and Technology· Computer Science
machine prediction:candidate · bibliometricsconsensus · none
7
citations
venueno affgemma · bibliometrics+open_sciencegpt · bibliometricsmodels split
Bibliometric Insights Into the Open Education Landscape
Rong Zou, Leilei Jiang, Walton Wider
2025· article· en· The International Review of Research in Open and Distributed Learning· Computer Science
machine prediction:candidate · bibliometricsconsensus · none
7
citations
affgemma · bibliometrics+metaresearchgpt · metaresearch+bibliometricsmodels split
Going beyond counting first authors in author co‐citation analysis
Dangzhi Zhao
2005· article· en· Proceedings of the American Society for Information Science and Technology· Decision Sciences
machine prediction:candidate · bibliometricsconsensus · none
7
citations
aboutno affgemma · metaresearch+bibliometricsgpt · bibliometricsmodels split
A Bibliometric Analysis of the Literature on Open Access in Scopus
Jenny Chung, Ming‐Yueh Tsay
2017· article· en· Qualitative and Quantitative Methods in Libraries· Decision Sciences
machine prediction:candidate · metaresearch+bibliometrics+open_scienceconsensus · none
7
citations
affgemma · metaresearch+research_integrity+bibliometricsgpt · scholarly_communication+research_integritymodels split
Disseminating Biomedical Research: Predatory Journals and Practices
Hassan Khan, Mona Ghannad, David Moher
2022· article· en· Indian Journal of Rheumatology· Decision Sciences
machine prediction:candidate · metaresearch+research_integrityconsensus · metaresearch
6
citations
aboutno affgemma · bibliometricsgpt · bibliometricsmodels agree
A bibliometric analysis of PCSK9 inhibitors from 2007 to 2022
Qin Luo, Zhenchu Tang, Panyun Wu, Zhangling Chen, Zhenfei Fang, Fei Luo
2023· article· en· Frontiers in Endocrinology· Medicine
machine prediction:candidate · bibliometricsconsensus · none
6
citations
aboutno affgemma · bibliometricsgpt · bibliometricsmodels split
Energy Efficiency Trends in Petroleum Extraction: A Bibliometric Study
Dauren Yessengaliyev, Yerlan Zhumagaliyev, A. Tazhibayev, Zhomart Bekbossynov, Zhadyrassyn Sarkulova, Gulya Issengaliyeva +4 more
2024· article· en· Energies· Energy
machine prediction:candidate · bibliometricsconsensus · none
6
citations
venueno affgemma · metaresearch+bibliometricsgpt · metaresearch+research_integrity+scholarly_communicationmodels split
An Evaluation of Primary Studies Published in Predatory Journals Included in Systematic Reviews From High-Impact Dermatology Journals: Cross-sectional Study
Ryan Ottwell, Brooke Hightower, Olivia Failla, Kelsey Snider, Adam Corcoran, Micah Hartwell +1 more
2022· article· en· JMIR Dermatology· Decision Sciences
machine prediction:candidate · metaresearch+bibliometricsconsensus · none
6
citations

How this was built: Screen · Findings · About