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
Robotic Path Planning Algorithms
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.

1,570 results · 1 filter active ·
Results by year
20002025
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.
1,570 works in the cohort · of 4,299,418page 17 of 32

Labels cover 1 of 1,570 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 1,570 of 1,570 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.

affno abstractunlabeled
Automated camera planning to film robot operations
Khaled Belghith, Froduald Kabanza, Philipe Bellefeuille, Leo Hartman
2011· article· en· Artificial Intelligence Review· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
venueno affunlabeled
Path Planning with Modified RRT* Algorithm for Lung Biopsy
Yuexi Dong, Kunpeng Wang, S.C. Fok, Han Wang
2022· article· en· Proceedings of the World Congress on Mechanical, Chemical, and Material Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affunlabeled
Azimut: a multimodal locomotion robotic platform
François Michaud, Dominic Létourneau, Martin Arsenault, Yann Bergeron, Richard Cadrin, F. Gagnon +7 more
2003· article· en· Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affunlabeled
Generating Paths with WFC
Hugo Scurti, Clark Verbrugge
2018· preprint· en· Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
afffundunlabeled
Multi-Robot Connected Fermat Spiral Coverage
Jingtao Tang, Hang Ma
2024· article· en· Proceedings of the International Conference on Automated Planning and Scheduling· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affno abstractunlabeled
Automated Contingency Management Design for UAVs
Jianhua Ge, Gregory J. Kacprzynski, Michael Roemer, George Vachtsevanos
2004· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affunlabeled
Path Finder in Unknown Environment
Faisal Shahzad, Raja Shahzad
2006· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations

How this was built: Screen · Findings · About