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
Lecture notes in computer 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.

11,332 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.
11,332 works in the cohort · of 4,299,418page 96 of 227

Labels cover 8 of 11,332 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 11,332 of 11,332 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
Transactions on Computational Science XVI
Marina L. Gavrilova
2012· book· en· Lecture notes in computer science· Decision Sciences
machine prediction:candidate · noneconsensus · none
6
citations
affno abstractunlabeled
Sketch Based Volumetric Clouds
Marc Stiver, Andrew B. Baker, Adam Runions, Faramarz Samavati
2010· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
affno abstractunlabeled
Performance Evaluation of 3D Keypoints and Descriptors
Zizui CHEN, Stephen Czarnuch, Andrew Smith, Mohamed Shehata
2016· book-chapter· en· Lecture notes in computer science· Engineering
machine prediction:candidate · noneconsensus · none
6
citations
affno abstractunlabeled
Scale-Aware RPN for Vehicle Detection
Lu Ding, Yong J. Wang, Robert Laganière, Xinbin Luo, Shan Fu
2018· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
affno abstractunlabeled
The Art of Shaving Logs
Timothy M. Chan
2013· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
affno abstractunlabeled
Efficient Codon Optimization with Motif Engineering
Anne Condon, Chris Thachuk
2011· book-chapter· en· Lecture notes in computer science· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
6
citations
affno abstractunlabeled
The One-Round Voronoi Game Replayed
Sándor P. Fekete, Henk Meijer
2003· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
affno abstractunlabeled
A Theoretical Analysis of Search in GSAT
Evgeny Skvortsov
2009· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
affno abstractunlabeled
Finding ID Attributes in XML Documents
Denilson Barbosa, Alberto O. Mendelzon
2003· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
affunlabeled
Textual User Requirements Notation
Ruchika Kumar, Gunter Mussbacher
2018· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
affno abstractunlabeled
Design efficient local search algorithms
Jun Gu
2005· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
affno abstractunlabeled
Peer Collaboration in Wireless Ad Hoc Networks
Lin Cai, Jianping Pan, Xuemin Shen, J.W. Mark
2005· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
affno abstractunlabeled
Analysis of Surfaces Using Constrained Regression Models
Sune Darkner, Mert R. Sabuncu, Polina Golland, Rasmus R. Paulsen, Rasmus Larsen
2008· article· en· Lecture notes in computer science· Neuroscience
machine prediction:candidate · noneconsensus · none
6
citations
afffundno abstractunlabeled
Batch Reinforcement Learning with State Importance
Lihong Li, Vadim Bulitko, Russell Greiner
2004· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations

How this was built: Screen · Findings · About