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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.

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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.

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Reference Services Review
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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.

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The four routes compose: require the funder route and exclude affiliation to get the funder-only stratum no affiliation-based frame ever sees.

59 results · 1 filter active ·
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20022024
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Machine labels · sparse coverage
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
59 works in the cohort · of 4,299,418page 1 of 2

Labels cover 0 of 59 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 59 of 59 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
The librarian's role in combating plagiarism
Nancy Gibson, Christina Chester‐Fangman
2011· article· en· Reference Services Review· Social Sciences
machine prediction:candidate · research_integrityconsensus · none
43
citations
affunlabeled
Wikipedia: friend or foe?
Kathy West, Janet Williamson
2009· article· en· Reference Services Review· Social Sciences
machine prediction:candidate · scholarly_communicationconsensus · none
32
citations
affunlabeled
Fake or for real? A fake news workshop
Katherine Hanz, Emily Kingsland
2020· article· en· Reference Services Review· Social Sciences
machine prediction:candidate · noneconsensus · none
26
citations
affaboutunlabeled
Getting everyone on the same page
Don MacMillan, Susan McKee, Shawna Sadler
2007· article· en· Reference Services Review· Computer Science
machine prediction:candidate · noneconsensus · none
13
citations
affaboutunlabeled
Patents under the microscope
Don MacMillan, Mindy Thuna
2010· article· en· Reference Services Review· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
8
citations
aboutno affunlabeled
Media literacy and newspapers of record
Scottie Kapel, Krista Schmidt
2018· article· en· Reference Services Review· Arts and Humanities
machine prediction:candidate · noneconsensus · none
7
citations
aboutno affunlabeled
Buy, borrow, or access online?
Diane Mizrachi
2016· article· en· Reference Services Review· Computer Science
machine prediction:candidate · scholarly_communicationconsensus · none
6
citations
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