MétaCan
ENFR
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.

Export CSVExport JSONAPI query

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
Coffee research and impacts
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.

598 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.
598 works in the cohort · of 4,299,418page 12 of 12

Labels cover 1 of 598 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 598 of 598 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
[no title]
Robert Lafrance
2010· article· en· International Review of Economics & Finance· Medicine
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
venueaboutno affunlabeled
Fair Trade Community Café Expansion
Karen Lightstone, Daphne Rixon
2014· article· en· Accounting Perspectives· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affno abstractunlabeled
Canada and Caffeine
Robby Gardner
2012· article· en· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Pharmacologic reactions to foods
Julia Upton
2023· book-chapter· en· Elsevier eBooks· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Assessing caffeine impact on patient health.
Chrissy Mouland, Laurie Nelmes, Sandy Harper-Jaques
2011· article· en· PubMed· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Factibilité De Valorisation Des Pulpes De Café Dans La Transition Energétique
Kanane Rusangwa Steven, Bisimwa Kalungwe Séraphin, Asifiwe Kadorho Rodrigue, Akilimali Zaramba Michel, Mweze Bagunda Jean-Marie, Mudekereza Kasenge Augustin +6 more
2025· article· International Journal of Science and Management Studies (IJSMS)· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
No rest for the stimmed: energy from caffeine or caffeine et al.?
Alyana Andal, Jason Curtis, Flavia Pereira, Gavin Poulshock, Hena Thakkar, Tehani Zambrano Castro +5 more
2025· article· en· Journal of the International Society of Sports Nutrition· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Le Cafe
2011· article· en· HELIN Digital Commons· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
← previous

How this was built: Screen · Findings · About