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
International Research Journal of Modernization in Engineering Technology and Science
Topic
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.

12 results · 1 filter active ·
Results by year
20232025
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.
12 works in the cohort · of 4,299,418page 1 of 1

Labels cover 0 of 12 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 12 of 12 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.

fundno affunlabeled
Artificial Intelligence in Education
Amarjeet Mallah, Mohan Devendra
2023· article· en· International Research Journal of Modernization in Engineering Technology and Science· Computer Science
machine prediction:candidate · noneconsensus · none
21
citations
affunlabeled
The Hospital Management System
2023· article· en· International Research Journal of Modernization in Engineering Technology and Science· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
9
citations
affunlabeled
KONKAN MEVA WEB PORTAL
Prasanna Kamat, Krunal Kadam, Chinmay Naik, Abhishek Pawar, Syed Ullah, Tania Allauddin +8 more
2023· article· en· International Research Journal of Modernization in Engineering Technology and Science· Business, Management and Accounting
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
affunlabeled
DESIGN AND OPTIMIZATION OF CHARCOAL ECONOMY ENERGY EFFICIENT STOVE
Okorun Ali, Agbadua Afokhainu, Adeniji Adeyemi, Marcus W. Dickson, Agina Chinaemelum
2023· article· en· International Research Journal of Modernization in Engineering Technology and Science· Engineering
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
BIG DATA OVER A DETECTED COMMUNITY
Jamal Bzai, Hisham Amin, Al Hejaji
2023· article· en· International Research Journal of Modernization in Engineering Technology and Science· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
NOVEL MECHANISM FOR STUDENT GRIEVANCE REDRESSAL
Er. Ashwini Meshram, Vedanti Palandurkar, Harshal Zade, Akash Masram, Nikita Manmode
2023· article· en· International Research Journal of Modernization in Engineering Technology and Science· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
WHY GEN-Z ARE FORGETTING THEIR CULTURES AND TRADITIONS
Ankit Sinhal, Riya Bohra, Shreya Darak, Vanshika Agarwal, Vansikha Choudhary
2023· article· en· International Research Journal of Modernization in Engineering Technology and Science· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About