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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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Parallel Computing and Optimization Techniques
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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.

affaffiliation
fundfunder
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.

2,094 results · 1 filter active ·
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20002025
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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.
2,094 works in the cohort · of 4,299,418page 19 of 42

Labels cover 1 of 2,094 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 2,094 of 2,094 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
BLUTune: Query-informed Multi-stage IBM Db2 Tuning via ML
Connor Henderson, Spencer Bryson, Vincent Corvinelli, Parke Godfrey, Piotr Mierzejewski, Jaroslaw Szlichta +1 more
2022· article· en· Proceedings of the 31st ACM International Conference on Information & Knowledge Management· Computer Science
machine prediction:candidate · noneconsensus · none
5
citations
affunlabeled
DynaSprint
Ziqiang Huang, José A. Joao, Alejandro Rico, Andrew D. Hilton, Benjamin C. Lee
2019· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
5
citations
affunlabeled
Pointy
Ioana Burcea, Livio Soares, Andreas Moshovos
2012· article· en· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
5
citations
afffundunlabeled
User-level Threading
Martin Karsten, Saman Barghi
2020· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
5
citations
aboutno affunlabeled
Multi-criteria Graph Partitioning with Scotch
Rémi Barat, Cédric Chevalier, François Pellegrini
2018· book-chapter· en· Society for Industrial and Applied Mathematics eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
5
citations
affunlabeled
Modeling Software Driven Power Consumption
Garnet Cameron
2006· article· en· 2005 IEEE Instrumentationand Measurement Technology Conference Proceedings· Computer Science
machine prediction:candidate · noneconsensus · none
5
citations
fundno affunlabeled
Fractal
Suvinay Subramanian, Mark C. Jeffrey, Maleen Abeydeera, Hyun Ryong Lee, Victor A. Ying, Joel Emer +1 more
2017· article· en· ACM SIGARCH Computer Architecture News· Computer Science
machine prediction:candidate · noneconsensus · none
5
citations

How this was built: Screen · Findings · About