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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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Electrospun Nanofibers in Biomedical Applications
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Abstract
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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.

1,209 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.
1,209 works in the cohort · of 4,299,418page 22 of 25

Labels cover 3 of 1,209 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 1,209 of 1,209 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
Macromol. Biosci. 10/2012
Shahed Al‐Haque, Jason W. Miklas, Nicole Feric, Loraine L. Y. Chiu, Wen Li Kelly Chen, Craig A. Simmons +1 more
2012· article· it· Macromolecular Bioscience· Materials Science
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
aboutno affunlabeled
Occurrence Download
2024· dataset· en· Global Biodiversity Information Facility· Materials Science
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affno abstractunlabeled
Concluding Insights
Yi Zhang
2024· book-chapter· en· Synthesis lectures on green energy and technology· Materials Science
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
afffundunlabeled
Adhesion and Binding in Nanofibrous Materials
Zhou Shufei, Wen Zhong
2010· article· en· Journal of Adhesion Science and Technology· Materials Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Bioactive nanofibers
Frank Ko, Yuqin Wan
2014· book-chapter· en· Cambridge University Press eBooks· Materials Science
machine prediction:candidate · noneconsensus · none
0
citations
venueno affunlabeled
Using Robotic Laser Ablation System to Study Cellular Stress Responses
David Z. He, S.H. Liu, Veronica Gomez‐Godinez, Zachary Wang, Chengbiao Wu, Linda Shi
2025· article· en· Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science· Materials Science
machine prediction:candidate · noneconsensus · none
0
citations
fundno affno abstractunlabeled
Issue Information: Macromol. Mater. Eng. 12/2025
2025· article· en· Macromolecular Materials and Engineering· Materials Science
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affno abstractunlabeled
Electrospinning for braiding
Ignacio Garcés, Cagri Ayranci, Jason P. Carey
2024· book-chapter· en· Elsevier eBooks· Materials Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Nanofiber-based diagnostics for cancer inflammation
Jasmine Kaur, Abhishek Konar, Pranabesh Ghosh, Mukul Machhindra Barwant, Pooja Kadyan
2025· book-chapter· en· Elsevier eBooks· Materials Science
machine prediction:candidate · noneconsensus · none
0
citations
venueno affunlabeled
Simulation and Experiment Analysis of Driveshaft
Jiahao Li, Yao Liu, Zhou Yang, You Zhe Wang, Zhan Ling Guo, Bin Shen
2023· article· en· Journal of Coating Science and Technology· Materials Science
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About