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
IoT and Edge/Fog Computing
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.

1,711 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.
1,711 works in the cohort · of 4,299,418page 30 of 35

Labels cover 5 of 1,711 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 1,711 of 1,711 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
Power-aware IoT Devices for the Future
Anju S. Pillai, Vijayalakshmi Saravanan, Kshirasagar Naik
2017· article· en· International Conference on Mobile Systems, Applications, and Services· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Adaptive Mobile Applications
Thomas Kunz
2001· book-chapter· en· IGI Global eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractgemma · bibliometricsgpt · bibliometricsmodels split
Kalman Filters in IoT: A Bibliometric Analysis
Khaled Obaideen, Mohammad Al‐Shabi, S. Andrew Gadsden
2025· book-chapter· en· Lecture notes in electrical engineering· Computer Science
machine prediction:candidate · bibliometricsconsensus · none
0
citations
affunlabeled
Guest Editorial
Sreeraman Rajan
2022· editorial· en· IEEE Instrumentation & Measurement Magazine· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
An Empirical Study of Ai Techniques in Mobile Applications
Yinghua Li, Xueqi Dang, Haoye Tian, Tiezhu Sun, Zhi-Jie Wang, Лей Ма +2 more
2024· preprint· en· SSRN Electronic Journal· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About