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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
fundfunder
venuejournal
aboutaboutness

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 6 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
Using clone detection to find malware in acrobat files
Saruhan Karademir, Thomas Dean, Sylvain Leblanc
2013· article· en· Conference of the Centre for Advanced Studies on Collaborative Research· Computer Science
machine prediction:candidate · noneconsensus · none
12
citations
affaboutunlabeled
Viruses 101
John Aycock, Ken Barker
2005· article· en· ACM SIGCSE Bulletin· Computer Science
machine prediction:candidate · noneconsensus · none
12
citations
affno abstractunlabeled
Artificial General Intelligence
2016· book· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
12
citations
afffundunlabeled
Micro‐ and Nano‐Bots for Infection Control
Azin Rashidy Ahmady, Shadman Khan, Hong Han, Wei Gao, Zeinab Hosseinidoust, Tohid F. Didar
2025· review· en· Advanced Materials· Computer Science
machine prediction:candidate · noneconsensus · none
12
citations
afffundunlabeled
TussleOS
Rayman Preet Singh, Benjamin Cassell, Srinivasan Keshav, Tim Brecht
2018· article· en· ACM SIGCOMM Computer Communication Review· Computer Science
machine prediction:candidate · noneconsensus · none
12
citations
affunlabeled
Evaluating security products with clinical trials
Anil Somayaji, Yiru Li, Hajime Inoue, José M. Fernandez, Richard Ford
2009· article· en· PolyPublie (École Polytechnique de Montréal)· Computer Science
machine prediction:candidate · metaresearchconsensus · none
11
citations
afffundunlabeled
LazyTainter
Zheng Wei, David Lie
2014· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
11
citations
affunlabeled
Least-Privilege Calls to Amazon Web Services
Puneet Gill, Werner Dietl, Mahesh Tripunitara
2022· article· en· IEEE Transactions on Dependable and Secure Computing· Computer Science
machine prediction:candidate · noneconsensus · none
11
citations
affno abstractunlabeled
Corrective Enforcement of Security Policies
Raphaël Khoury, Nadia Tawbi
2011· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
11
citations
afffundno abstractunlabeled
Empirical Study on REST APIs Usage in Android Mobile Applications
Mohamed A. Oumaziz, Abdelkarim Belkhir, Tristan Vacher, Éric Beaudry, Xavier Blanc, Jean‐Rémy Falleri +1 more
2017· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
10
citations
afffundunlabeled
Black Market Botnets
Nathan Friess, John Aycock
2007· article· en· PRISM (University of Calgary)· Computer Science
machine prediction:candidate · noneconsensus · none
10
citations
affunlabeled
A Comparative Study of Software Bugs in Clone and Non-Clone Code
Judith F. Islam, Manishankar Mondal, Chanchal K. Roy, Kevin A. Schneider
2017· article· en· Proceedings/Proceedings of the ... International Conference on Software Engineering and Knowledge Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
10
citations
affno abstractunlabeled
A Study of Children Facial Recognition for Privacy in Smart TV
Patrick C. K. Hung, Kamen Kanev, Farkhund Iqbal, David Mettrick, Laura Rafferty, Guan-Pu Pan +2 more
2017· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
9
citations
affno abstractunlabeled
Monitoring of Security Properties Using BeepBeep
Mohamed Recem Boussaha, Raphaël Khoury, Sylvain Hallé
2018· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
9
citations

How this was built: Screen · Findings · About