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
Mass Spectrometry Techniques and Applications
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.

2,738 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.
2,738 works in the cohort · of 4,299,418page 41 of 55

Labels cover 3 of 2,738 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 2,738 of 2,738 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.

affunlabeled
Technology Development: an Overview
Christoph H. Borchers
2010· article· en· Molecular & Cellular Proteomics· Chemistry
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Time of Flight Mass Spectrometers
K.G. Standing, W. Ens
2016· book-chapter· en· Elsevier eBooks· Chemistry
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Mass Spectrometry
Hélène Perreault, Erika Lattová
2011· book-chapter· en· Elsevier eBooks· Chemistry
machine prediction:candidate · noneconsensus · none
1
citations
fundno affunlabeled
The analysis of organic ballistic materials
OJ Dalby
2011· dissertation· en· Nottingham Trent University's Institutional Repository (Nottingham Trent Repository)· Chemistry
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Mass Spectrometry
Hélène Perreault, Erika Lattová
2011· book-chapter· en· Elsevier eBooks· Chemistry
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Unified Atomic Mass Unit
Kermit K. Murray, Robert K. Boyd, Marcos N. Eberlin, G. John Langley, Liang Li, Yasuhide Naito
2016· dataset· en· IUPAC Standards Online· Chemistry
machine prediction:candidate · noneconsensus · none
1
citations
afffundunlabeled
How soft is your ESI-MS anyway?
Ian C. Chagunda, Peter J. H. Williams, Tiago Fisher, Naomi L. Stock, Daniel G. Beach, Gilian T. Thomas +2 more
2024· preprint· en· ChemRxiv· Chemistry
machine prediction:candidate · noneconsensus · none
1
citations
aboutno affunlabeled
Sunset on lake at Tetlin NWR
2013· other· en· Chemistry
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations

How this was built: Screen · Findings · About