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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 35 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.

affunlabeled
Appendix for RevealNet
2025· article· en· Zenodo (CERN European Organization for Nuclear Research)· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affno abstractunlabeled
Big Data in Network Anomaly Detection
Duc C. Le, A. Nur Zincir‐Heywood
2012· book-chapter· en· Encyclopedia of Big Data Technologies· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Online detection of anomalous applications on the cloud.
Arnamoy Bhattacharyya, Harsh Vikram Singh, Seyed Ali Jokar Jandaghi, Cristiana Amza
2017· article· en· Conference of the Centre for Advanced Studies on Collaborative Research· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Secure Architectures with Active Networks
Srinivas Sampalli, Yaser Haggag, C. Labonte
2006· other· en· Network Security· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
RQPool: A Novel Multi-Branch Graph-Level Anomaly Detection
Aaron Alex Philip, Ziad Kobti
2025· article· en· Proceedings of the ... International Florida Artificial Intelligence Research Society Conference· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Optimized Feature Selection for Network Anomaly Detection
Aniss Chohra, Paria Shirani, ElMouatez Billah Karbab, Mourad Debbabi
2023· book-chapter· en· World Scientific series in digital forensics and cybersecurity· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Multi-level Security Investigation for Clouds
Suryadipta Majumdar
2023· book-chapter· en· World Scientific series in digital forensics and cybersecurity· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations

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