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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.

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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PeerJ Computer Science
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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.

82 results · 1 filter active ·
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20152025
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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.
82 works in the cohort · of 4,299,418page 1 of 2

Labels cover 7 of 82 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 82 of 82 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.

fundno affunlabeled
Software citation principles
Arfon M. Smith, Daniel S. Katz, Kyle E. Niemeyer
2016· article· en· PeerJ Computer Science· Decision Sciences
machine prediction:candidate · metaresearch+scholarly_communicationconsensus · none
269
citations
affunlabeled
Sustainable computational science: the ReScience initiative
Konrad Hinsen, Frédéric Alexandre, Thomas Arildsen, Lorena A. Barba, Fabien Benureau, C. Titus Brown +38 more
2017· article· en· PeerJ Computer Science· Decision Sciences
machine prediction:candidate · open_scienceconsensus · none
127
citations
affunlabeled
Deep learning methods for inverse problems
Shima Kamyab, Zohreh Azimifar, Rasool Sabzi, Paul Fieguth
2022· article· en· PeerJ Computer Science· Engineering
machine prediction:candidate · noneconsensus · none
28
citations
affunlabeled
A longitudinal study of topic classification on Twitter
Mohamed Reda Bouadjenek, Scott Sanner, Zahra Iman, Lexing Xie, Daniel Xiaoliang Shi
2022· article· en· PeerJ Computer Science· Physics and Astronomy
machine prediction:candidate · noneconsensus · none
8
citations

How this was built: Screen · Findings · About