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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.

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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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Spam and Phishing Detection
Retraction
Abstract
Evidence source
Study design
Label agreement
Label status

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.

516 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.
516 works in the cohort · of 4,299,418page 5 of 11

Labels cover 1 of 516 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 516 of 516 works in this cohort. Predictions are machine_predicted_unvalidated teacher distillation outputs. Candidate is the union; consensus is the intersection.

aboutno affunlabeled
Analysis of Student Vulnerabilities to Phishing.
Janet L. Bailey, Robert B. Mitchell, Bradley K. Jensen
2008· article· en· Americas Conference on Information Systems· Computer Science
distilled prediction:candidate · noneconsensus · none
7
citations
affno abstractunlabeled
Phishing Attacks Modifications and Evolutions
Qian Cui, Guy-Vincent Jourdan, Gregor von Bochmann, Iosif-Viorel Onut, Jason Flood
2018· book-chapter· en· Lecture notes in computer science· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
7
citations
affunlabeled
Phishing Kits Source Code Similarity Distribution: A Case Study
Ettore Merlo, Mathieu Margier, Guy-Vincent Jourdan, Iosif-Viorel Onut
2022· article· en· 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
6
citations
affunlabeled
Phishing Attacks Over Time: A Longitudinal Study
Albert L. Harris, Dave Yates
2015· article· en· Journal of the Association for Information Systems· Computer Science
distilled prediction:candidate · noneconsensus · none
5
citations
fundno affunlabeled
Estimating the Credibility of Examples in Automatic Document Classification
João Palotti, Thiago Salles, Gisele L. Pappa, Filipe de Lima Arcanjo, Marcos André Gonçalves, Wagner Meira
2010· article· en· Cadernos de Linguística e Teoria da Literatura (Universidade Federal de Minas Gerais)· Computer Science
distilled prediction:candidate · scholarly_communicationconsensus · none
4
citations
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How this was built: Screen · Findings · About