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
Flow Measurement and Analysis
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.

321 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.
321 works in the cohort · of 4,299,418page 5 of 7

Labels cover 0 of 321 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 321 of 321 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.

venueno affunlabeled
On the Basic Parameters of Pipe Measuring Device
V. F. Shayakberov, E. V. Shayakberov
2014· article· en· Advances in petroleum exploration and development· Engineering
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
10.1063/1.3650767.8
2011· dataset· en· Default Digital Object Group· Engineering
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affunlabeled
Optimization Of A Multi-Jet Water Flow Meter
Mitchell L Boddy, Eric Savory
2021· article· en· Progress in Canadian Mechanical Engineering. Volume 4· Engineering
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
10.1063/1.3650767.12
2011· dataset· en· Default Digital Object Group· Engineering
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
venueno affno abstractunlabeled
10.1016/s0967-0653(97)83613-7
2000· article· en· Time to knit· Engineering
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
venueno affno abstractunlabeled
10.1016/s0967-0653(97)85981-9
2000· article· en· Time to knit· Engineering
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
aboutno affunlabeled
Numerical Simulation of Bubbly Flow in Laval Nozzle
Naohiro ABIKO, Hirofumi Sugawara, Ryu EGASHIRA, Takeru Yano, Shigeo FUJIKAWA
2004· article· en· The proceedings of the JSME annual meeting· Engineering
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Fluid Metering
G.S. Patience
2017· book-chapter· en· Elsevier eBooks· Engineering
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
LIGO/Virgo S190814bv: CFHTphotometryof AT2019npv
X. Wang, S. Antier, M. W. Coughlin, W. Li, Xianfei Zhang, J. Mo +33 more
2019· article· en· UWA Profiles and Research Repository (University of Western Australia)· Engineering
machine prediction:candidate · noneconsensus · none
0
citations
venueno affno abstractunlabeled
10.1016/0967-0653(95)96753-r
2000· article· en· Time to knit· Engineering
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
affunlabeled
Measurement of Flow Pressure
Stavros Tavoularis, Jovan Nedić
2024· book-chapter· en· Cambridge University Press eBooks· Engineering
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Measurement of Flow Rate
2024· book-chapter· en· Cambridge University Press eBooks· Engineering
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Full-Time Equivalent (FTE) Numbers
David Birnbaum
2002· letter· en· Infection Control and Hospital Epidemiology· Engineering
machine prediction:candidate · noneconsensus · none
0
citations
venueno affunlabeled
High Frequency Flow Measurement Technique for Slug Flow Regimes
Seyyed Saeed Shojaee Zadeh, Vanessa Egan, Pat Walsh
2022· article· en· Proceedings of the World Congress on Mechanical, Chemical, and Material Engineering· Engineering
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Mean Filter
Dongqing Li
2015· book-chapter· en· Engineering
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
10.1063/1.3650767.6
M. Khammar, John M. Shaw
2011· dataset· en· Default Digital Object Group· Engineering
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations

How this was built: Screen · Findings · About