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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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Network Security and Intrusion Detection
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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.

1,999 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.
1,999 works in the cohort · of 4,299,418page 33 of 40

Labels cover 2 of 1,999 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 1,999 of 1,999 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.

aboutno affunlabeled
histoire des bibliothèques pdf
2024· other· fr· Zenodo (CERN European Organization for Nuclear Research)· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
affunlabeled
Voice Over IP Security
Son T. Vuong, Kapil Singh
2006· other· en· Network Security· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Decision Systems Optimization for Cybersecurity Resilience
Sharayu Ikhar, Dipannita Mondal, Deepti Khubalkar, Pallavi Deshpande, Surabhi B. Patil
2025· book-chapter· en· Smart innovation, systems and technologies· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Network Anomaly Detection System Using an Attention-Based GNN
Amir Mohammadi Bagha, Isaac Woungang, Issa Traoré, Danda B. Rawat, Sudeep Tanwar, Azin Hassanalizadeh
2025· book-chapter· en· Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Suicide Attacks on the Rise
2008· article· en· Defense Technical Information Center (DTIC)· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Big Data in Network Anomaly Detection
Duc C. Le, A. Nur Zincir‐Heywood
2019· book-chapter· en· Encyclopedia of Big Data Technologies· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Cyber-Bullying and Harassment Detection using ML
Omkar Horate
2025· article· en· International Journal for Research in Applied Science and Engineering Technology· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
fundno affno abstractunlabeled
Disjoint dijoins
2016· article· en· Oxford University Research Archive (ORA) (University of Oxford)· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
venueno affunlabeled
10.51847/tD5eylpK6G
2000· article· en· Time to knit· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations

How this was built: Screen · Findings · About