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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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Herpesvirus Infections and Treatments
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
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venuejournal
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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.

1,786 results · 1 filter active ·
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20002025
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Machine labels · sparse coverage
Evidence
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
1,786 works in the cohort · of 4,299,418page 32 of 36

Labels cover 6 of 1,786 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 1,786 of 1,786 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
les perdants magnifiques pdf
2024· other· fr· Zenodo (CERN European Organization for Nuclear Research)· Medicine
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
venueno affno abstractunlabeled
10.1016/s1553-3212(07)70280-7
2000· article· en· Time to knit· Medicine
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
venueno affno abstractunlabeled
10.1016/b978-2-294-02099-5.50003-0
2000· book-chapter· en· Time to knit· Medicine
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
venueno affno abstractunlabeled
10.1016/s0029-7437(07)70707-3
2000· article· en· Time to knit· Medicine
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
aboutno affunlabeled
Factors Influencing Uptake
2016· article· en· Medicine
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
venueno affno abstractunlabeled
10.1016/j.ymed.2013.07.048
2000· article· en· Time to knit· Medicine
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
affno abstractunlabeled
Reply
Amir Hossein Massoud, Gabriel Kaufman, Ciriaco A. Piccirillo, Bruce Mazer
2014· letter· en· Journal of Allergy and Clinical Immunology· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Elderly Man With Abdominal Rash
Roxana Mititelu, Anar Mikailov
2016· article· en· Annals of Emergency Medicine· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Herpes genitalis
2025· book-chapter· de· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Beseiged
Maya Haasz, Michael Bonnycastle, Joe Dylewski, Lucie Opatrny
2006· article· en· The American Journal of Medicine· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Reply to McShane
Philip C. Hill, Frank Cobelens, Leonardo Martínez, Alberto L. García‐Basteiro, Marcel A. Behr, Molebogeng X. Rangaka +4 more
2023· letter· en· The Journal of Infectious Diseases· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
venueno affno abstractunlabeled
10.1016/s0029-7437(06)71625-1
2000· article· en· Time to knit· Medicine
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
affunlabeled
MR frequency differentiates MS lesion severity (P6.114)
Shannon Kolind, Vanessa Wiggermann, Samantha Tan, Enedino Hernández Torres, David Li, Nicolas Seneca +4 more
2015· article· en· Neurology· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
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How this was built: Screen · Findings · About