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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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ACM Transactions on Modeling and Performance Evaluation of Computing Systems
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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
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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.

19 results · 1 filter active ·
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20162025
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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.
19 works in the cohort · of 4,299,418page 1 of 1

Labels cover 0 of 19 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 19 of 19 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
Quantifying Cloud Performance and Dependability
Nikolas Herbst, André Bauer, Samuel Kounev, Giorgos Oikonomou, Erwin van Eyk, George Kousiouris +5 more
2018· article· en· ACM Transactions on Modeling and Performance Evaluation of Computing Systems· Computer Science
machine prediction:candidate · noneconsensus · none
35
citations
affunlabeled
Performance Analysis of the IOTA DAG-Based Distributed Ledger
Caixiang Fan, Sara Ghaemi, Hamzeh Khazaei, Yuxiang Chen, Petr Musı́lek
2021· article· en· ACM Transactions on Modeling and Performance Evaluation of Computing Systems· Computer Science
machine prediction:candidate · noneconsensus · none
26
citations
affunlabeled
Cocoa
Xiaomeng Yi, Fangming Liu, Di Niu, Hai Jin, John C. S. Lui
2017· article· en· ACM Transactions on Modeling and Performance Evaluation of Computing Systems· Computer Science
machine prediction:candidate · noneconsensus · none
15
citations
afffundunlabeled
Employing Software-Managed Caches in OpenACC
Ahmad Lashgar, Amirali Baniasadi
2016· article· en· ACM Transactions on Modeling and Performance Evaluation of Computing Systems· Computer Science
machine prediction:candidate · noneconsensus · none
7
citations
affunlabeled
Fast Power and Energy Management for Future Many-Core Systems
Yanpei Liu, Guilherme Cox, Qingyuan Deng, Stark C. Draper, Ricardo Bianchini
2017· article· en· ACM Transactions on Modeling and Performance Evaluation of Computing Systems· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affunlabeled
Focused Layered Performance Modelling by Aggregation
Farhana Islam, Dorina C. Petriu, Murray Woodside
2022· article· en· ACM Transactions on Modeling and Performance Evaluation of Computing Systems· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
AMIR
Amir Kalbasi, Diwakar Krishnamurthy, Jerry Rolia
2019· article· en· ACM Transactions on Modeling and Performance Evaluation of Computing Systems· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
1
citations
aboutno affunlabeled
Big Winners and Small Losers of Zero-rating
Niloofar Bayat, T. B. Richard, Vishal Misra, Dan Rubenstein
2022· article· en· ACM Transactions on Modeling and Performance Evaluation of Computing Systems· Engineering
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About