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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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Software System Performance and Reliability
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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.

1,099 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.
1,099 works in the cohort · of 4,299,418page 2 of 22

Labels cover 1 of 1,099 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 1,099 of 1,099 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.

afffundunlabeled
Behavioral resource-aware model inference
Tony Ohmann, Michael Herzberg, Sebastian Fiss, Armand Halbert, Marc Palyart, Ivan Beschastnikh +1 more
2014· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
56
citations
affunlabeled
Diagnosing mobile ad-hoc networks
Mourad Elhadef, Azzedine Boukerche, Hisham Elkadiki
2006· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
55
citations
affunlabeled
Autonomic load-testing framework
Cornel Barna, Marin Litoiu, Hamoun Ghanbari
2011· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
46
citations
afffundunlabeled
Pensieve
Yongle Zhang, Serguei Makarov, Xiang Ren, David Lion, Ding Yuan
2017· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
43
citations
afffundunlabeled
Visualizing Distributed System Executions
Ivan Beschastnikh, Perry Liu, Albert Xing, Patty Wang, Yuriy Brun, Michael D. Ernst
2020· article· en· ACM Transactions on Software Engineering and Methodology· Computer Science
machine prediction:candidate · noneconsensus · none
41
citations
affunlabeled
Where shall we log?
Zhenhao Li, Tse-Hsun Chen, Weiyi Shang
2020· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
41
citations
affunlabeled
A Survey of Software Log Instrumentation
Boyuan Chen, Zhen Ming Jiang
2021· review· en· ACM Computing Surveys· Computer Science
machine prediction:candidate · noneconsensus · none
39
citations
affunlabeled
LogStamp
Shimin Tao, Weibin Meng, Yimeng Cheng, Yichen Zhu, Ying Liu, Chunning Du +4 more
2022· article· en· ACM SIGMETRICS Performance Evaluation Review· Computer Science
machine prediction:candidate · noneconsensus · none
38
citations
affno abstractunlabeled
Performance Analysis with UML
Dorina C. Petriu, C.M. Woodside
2003· book-chapter· en· Computer Science
machine prediction:candidate · noneconsensus · none
38
citations

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