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
Sensory Analysis and Statistical Methods
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.

428 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.
428 works in the cohort · of 4,299,418page 5 of 9

Labels cover 0 of 428 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 428 of 428 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.

afffundunlabeled
Parallel coordinate order for<scp>high‐dimensional</scp>data
Shaima Tilouche, Vahid Partovi Nia, Samuel Bassetto
2021· article· en· Statistical Analysis and Data Mining The ASA Data Science Journal· Agricultural and Biological Sciences
machine prediction:candidate · noneconsensus · none
7
citations
afffundunlabeled
Dual Scaling for the Analysis of Categorical Data
Michael D. Maraun, Kathleen L. Slaney, Jarkko Jalava
2005· article· en· Journal of Personality Assessment· Agricultural and Biological Sciences
machine prediction:candidate · noneconsensus · none
7
citations
affunlabeled
Appendix F: Mean versus Scale Mid-Point
Sandra Pitts
2009· book-chapter· en· Agricultural and Biological Sciences
machine prediction:candidate · insufficient_payloadconsensus · none
6
citations
affunlabeled
Redundancy Analysis
Wayne S. DeSarbo, Heungsun Hwang, Kamel Jedidi
2016· other· en· Wiley StatsRef: Statistics Reference Online· Agricultural and Biological Sciences
machine prediction:candidate · noneconsensus · none
5
citations
affunlabeled
Rejoinder: fractures in the edifice of PLS
Mikko Rönkkö, Nick Lee, Jöerg Evermann, Cameron N. McIntosh, John Antonakis
2023· article· en· European Journal of Marketing· Agricultural and Biological Sciences
machine prediction:candidate · metaresearchconsensus · none
5
citations
affno abstractunlabeled
Sensory analysis of food flavor
Ann C. Noble, Isabelle Lesschaeve
2006· book-chapter· it· Elsevier eBooks· Agricultural and Biological Sciences
machine prediction:candidate · noneconsensus · none
4
citations
afffundno abstractunlabeled
Some new aspects of taxicab correspondence analysis
Vartan Choulakian, Biagio Simonetti, Thu Pham Gia
2014· article· en· Statistical Methods & Applications· Agricultural and Biological Sciences
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
Modeling of Food Choice
C. Peter Herman, Janet Polivy, Patricia Pliner, Lenny R. Vartanian
2019· book-chapter· en· Agricultural and Biological Sciences
machine prediction:candidate · noneconsensus · none
3
citations

How this was built: Screen · Findings · About