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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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Advanced Malware Detection 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
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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.

977 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.
977 works in the cohort · of 4,299,418page 5 of 20

Labels cover 1 of 977 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 977 of 977 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
Android Platform Malware Analysis
Khalid Alfalqi, Rubayyi Alghamdi, Mofareh Waqdan
2015· article· en· International Journal of Advanced Computer Science and Applications· Computer Science
machine prediction:candidate · noneconsensus · none
17
citations
affno abstractunlabeled
Patttern-Based AI Scripting Using ScriptEase
Matthew McNaughton, J. Redford, Jonathan Schaeffer, Duane Szafron
2003· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
16
citations
affaboutunlabeled
Viruses 101
John Aycock, K. Barker
2005· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
15
citations
afffundunlabeled
Papilio: Visualizing Android Application Permissions
Mona Hosseinkhani Loorak, Patrick S.W. Fong, Sheelagh Carpendale
2014· article· en· Computer Graphics Forum· Computer Science
machine prediction:candidate · noneconsensus · none
15
citations
affunlabeled
ToGather
ElMouatez Billah Karbab, Mourad Debbabi
2018· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
14
citations
affunlabeled
Identification of Android malware using refined system calls
K. Deepa, G. Radhamani, P. Vinod, Mohammad Shojafar, Neeraj Kumar, Mauro Conti
2019· article· en· Concurrency and Computation Practice and Experience· Computer Science
machine prediction:candidate · noneconsensus · none
14
citations
affunlabeled
"Good" worms and human rights
John Aycock, Alana Maurushat
2008· article· en· ACM SIGCAS Computers and Society· Computer Science
machine prediction:candidate · noneconsensus · none
14
citations
affunlabeled
A First Look into Software Security Practices in Bangladesh
Ankit Shrestha, Tanusree Sharma, Pratyasha Saha, Syed Ishtiaque Ahmed, Mahdi Nasrullah Al-Ameen
2023· article· en· ACM Journal on Computing and Sustainable Societies· Computer Science
machine prediction:candidate · noneconsensus · none
14
citations
affunlabeled
Rotten apples spoil the bunch
Michael C. Cao, Khaled E. Ahmed, Julia Rubin
2022· article· en· Proceedings of the 44th International Conference on Software Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
14
citations
affno abstractunlabeled
Horus: A Security Assessment Framework for Android Crypto Wallets
Md Shahab Uddin, Mohammad Mannan, Amr Youssef
2021· book-chapter· en· Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
13
citations
affno abstractunlabeled
Towards Sound Detection of Virtual Machines
Jason Franklin, Mark Luk, Jonathan M. McCune, Arvind Seshadri, Adrian Perrig, Leendert van Doorn
2007· book-chapter· en· Advances in information security· Computer Science
machine prediction:candidate · noneconsensus · none
13
citations
afffundunlabeled
A Deep Learning Framework for Malware Classification
Mahmoud Kalash, Mrigank Rochan, Noman Mohammed, Neil D. B. Bruce, Yang Wang, Farkhund Iqbal
2019· article· en· International Journal of Digital Crime and Forensics· Computer Science
machine prediction:candidate · noneconsensus · none
13
citations
afffundno abstractunlabeled
An empirical study of Android Wear user complaints
Suhaib Mujahid, Giancarlo Sierra, Rabe Abdalkareem, Emad Shihab, Weiyi Shang
2018· article· en· Empirical Software Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
13
citations

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