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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 SAR Imaging 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.

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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.

294 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.
294 works in the cohort · of 4,299,418page 2 of 6

Labels cover 0 of 294 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 294 of 294 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
Adaptive S-Method for SAR/ISAR Imaging
Ljubiša Stanković, T. Thayaparan, Vesna Popović–Bugarin, Igor Djurović, Miloš Daković
2007· article· en· EURASIP Journal on Advances in Signal Processing· Engineering
machine prediction:candidate · noneconsensus · none
18
citations
affunlabeled
Joint Design for RIS-Aided ISAC via Deep Unfolding Learning
Jifa Zhang, Mingqian Liu, Jie Tang, Nan Zhao, Dusit Niyato, Xianbin Wang
2024· article· en· IEEE Transactions on Cognitive Communications and Networking· Engineering
machine prediction:candidate · noneconsensus · none
16
citations
affunlabeled
Multirotor UAV-Borne Repeat-Pass CSM-VideoSAR
Ying Zhang, Daiyin Zhu, Xinhua Mao, Gong Zhang, Henry Leung
2021· article· en· IEEE Transactions on Aerospace and Electronic Systems· Engineering
machine prediction:candidate · noneconsensus · none
14
citations
affaboutunlabeled
Demonstrations of HRWS and GMTI with RADARSAT-2
Ishuwa Sikaneta, Delphine Cerutti‐Maori
2012· article· en· Synthetic Aperture Radar, 2012. EUSAR. 9th European Conference on· Engineering
machine prediction:candidate · noneconsensus · none
14
citations
affunlabeled
S-Method In Radar Imaging
2006· article· en· Zenodo (CERN European Organization for Nuclear Research)· Engineering
machine prediction:candidate · noneconsensus · none
11
citations
affunlabeled
A global review of optronic synthetic aperture radar/ladar processing
Linda Marchese, Michel Doucet, Pascal Bourqui, Bernd Harnisch, Martin Süess, Mathieu Legros +8 more
2013· review· en· Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE· Engineering
machine prediction:candidate · noneconsensus · none
8
citations
affunlabeled
Ship detection with spaceborne multi-channel SAR/GMTI radars
Delphine Cerutti‐Maori, Ishuwa Sikaneta, Christoph H. Gierull
2012· article· en· Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft)· Engineering
machine prediction:candidate · noneconsensus · none
8
citations

How this was built: Screen · Findings · About