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
AgEcon Search (University of Minnesota, USA)
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.

726 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.
726 works in the cohort · of 4,299,418page 9 of 15

Labels cover 0 of 726 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 726 of 726 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.

aboutno affunlabeled
Risking-sharing Efficiency of Hedging Strategies
G. Cornelis van Kooten, Changhao Guo, Baojing Sun
2015· article· en· AgEcon Search (University of Minnesota, USA)· Agricultural and Biological Sciences
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
FARMLAND VALUES AND CREDIT CONDITIONS
2023· other· en· AgEcon Search (University of Minnesota, USA)· Agricultural and Biological Sciences
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
A Statistical Profile of the Pork Supply Chain
Samuel Bonti‐Ankomah
2005· article· en· AgEcon Search (University of Minnesota, USA)· Agricultural and Biological Sciences
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
AGRICULTURE IN CANADA: WHO WILL GROW THE FOOD?
Mel L. Lerohl, James R. Unterschultz
2000· article· en· AgEcon Search (University of Minnesota, USA)· Agricultural and Biological Sciences
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
DISCUSSION: MANITOBA GRAIN AND LIVESTOCK FARMER
Owen McAuley
2001· preprint· en· AgEcon Search (University of Minnesota, USA)· Agricultural and Biological Sciences
machine prediction:candidate · noneconsensus · none
0
citations
fundno affunlabeled
Artificial Regressions
2001· article· en· AgEcon Search (University of Minnesota, USA)· Mathematics
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Ag Letter
2009· article· en· AgEcon Search (University of Minnesota, USA)· Agricultural and Biological Sciences
machine prediction:candidate · noneconsensus · none
0
citations
fundno affunlabeled
COVER AND CONTENTS PAGES
2017· article· en· AgEcon Search (University of Minnesota, USA)· Economics, Econometrics and Finance
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations

How this was built: Screen · Findings · About