MétaCan
Menu
Cohort builder

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.

Search term
Author
Year range
Sort
Language
Type
Field
Venue
Topic
UAV Applications and Optimization
Retraction
Abstract
Evidence source
Study design
Label agreement
Label status

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.

887 results · 1 filter active ·
Results by year
20012025
Publication date
Categories
Machine labels · sparse coverage
Evidence
Language
Type
Citations
An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
887 works in the cohort · of 4,299,418page 1 of 18

Labels cover 1 of 887 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 887 of 887 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
Airborne Communication Networks: A Survey
Xianbin Cao, Peng Yang, Mohamed Alzenad, Xing Xi, Dapeng Wu, Halim Yanıkömeroğlu
2018· article· en· IEEE Journal on Selected Areas in Communications· Engineering
machine prediction:candidate · noneconsensus · none
336
citations
affunlabeled
Drone Deep Reinforcement Learning: A Review
Ahmad Taher Azar, Anis Koubâa, Nada Ali Mohamed, Habiba A. Ibrahim, Zahra Fathy Ibrahim, Muhammad Kazim +5 more
2021· review· en· Electronics· Engineering
machine prediction:candidate · noneconsensus · none
300
citations
affunlabeled
Green UAV communications for 6G: A survey
Xu Jiang, Min Sheng, Nan Zhao, Chengwen Xing, Weidang Lu, Xianbin Wang
2021· article· en· Chinese Journal of Aeronautics· Engineering
machine prediction:candidate · noneconsensus · none
198
citations
affunlabeled
Placement and Power Allocation for NOMA-UAV Networks
Xiaonan Liu, Jingjing Wang, Nan Zhao, Yunfei Chen, Shun Zhang, Zhiguo Ding +1 more
2019· article· en· IEEE Wireless Communications Letters· Engineering
machine prediction:candidate · noneconsensus · none
191
citations
affno abstractunlabeled
UAV-assisted data gathering in wireless sensor networks
Mianxiong Dong, Kaoru Ota, Man Lin, Zunyi Tang, Suguo Du, Haojin Zhu
2014· article· en· The Journal of Supercomputing· Engineering
machine prediction:candidate · noneconsensus · none
166
citations
affunlabeled
Application-driven design of aerial communication networks
Torsten Andre, Karin Anna Hummel, Angela P. Schoellig, Evşen Yanmaz, Mahdi Asadpour, Christian Bettstetter +4 more
2014· article· en· IEEE Communications Magazine· Engineering
machine prediction:candidate · noneconsensus · none
143
citations
affunlabeled
The Drone Scheduling Problem: A Systematic State-of-the-Art Review
Junayed Pasha, Zeinab Elmi, Sumit Purkayastha, Amir M. Fathollahi‐Fard, Ying-En Ge, Yui‐yip Lau +1 more
2022· article· en· IEEE Transactions on Intelligent Transportation Systems· Engineering
machine prediction:candidate · noneconsensus · none
134
citations
affunlabeled
Weather constraints on global drone flyability
Mozhou Gao, Chris H. Hugenholtz, T. A. Fox, Maja Kucharczyk, Thomas E. Barchyn, Paul R. Nesbit
2021· article· en· Scientific Reports· Engineering
machine prediction:candidate · noneconsensus · none
130
citations

How this was built: Screen · Findings · About