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
Trends in Amplification
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.

18 results · 1 filter active ·
Results by year
20022013
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.
18 works in the cohort · of 4,299,418page 1 of 1

Labels cover 0 of 18 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 18 of 18 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
The Desired Sensation Level Multistage Input/Output Algorithm
Susan Scollie, Richard C. Seewald, Leonard E. Cornelisse, Sheila Moodie, Marlene Bagatto, Diana Laurnagaray +2 more
2005· review· en· Trends in Amplification· Neuroscience
machine prediction:candidate · noneconsensus · none
390
citations
affunlabeled
Hearing Aids and Music
Marshall Chasin, Frank Russo
2004· review· en· Trends in Amplification· Neuroscience
machine prediction:candidate · noneconsensus · none
100
citations
affunlabeled
Nonlinear Frequency Compression
Vijay Parsa, Susan Scollie, Danielle Glista, Andreas Seelisch
2013· article· en· Trends in Amplification· Neuroscience
machine prediction:candidate · noneconsensus · none
44
citations
afffundunlabeled
Knowledge Translation in Audiology
Sheila Moodie, Anita Kothari, Marlene Bagatto, Richard C. Seewald, Linda T. Miller, Susan Scollie
2011· review· en· Trends in Amplification· Health Professions
machine prediction:candidate · noneconsensus · none
43
citations
afffundaboutunlabeled
An Integrated Knowledge Translation Experience
Sheila Moodie, Marlene Bagatto, Linda T. Miller, Anita Kothari, Richard C. Seewald, Susan Scollie
2011· article· en· Trends in Amplification· Neuroscience
machine prediction:candidate · noneconsensus · none
17
citations
aboutno affunlabeled
The Need for Evidence in an Anecdotal World
Charles J. Limb
2011· editorial· en· Trends in Amplification· Decision Sciences
machine prediction:candidate · metaresearchconsensus · metaresearch
5
citations

How this was built: Screen · Findings · About