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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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Spam and Phishing Detection
Retraction
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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.

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 1 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.

afffundunlabeled
Deception detection for news: Three types of fakes
Victoria L. Rubin, Yimin Chen, Nadia Conroy
2015· article· en· Proceedings of the Association for Information Science and Technology· Computer Science
distilled prediction:candidate · noneconsensus · none
544
citations
affunlabeled
The socialbot network
Yazan Boshmaf, Ildar Muslukhov, Konstantin Beznosov, Matei Ripeanu
2011· article· en· Computer Science
distilled prediction:candidate · noneconsensus · none
442
citations
affunlabeled
Email Spam Filtering: A Systematic Review
Gordon V. Cormack
2008· review· en· Foundations and Trends® in Information Retrieval· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
254
citations
afffundno abstractunlabeled
Cost-sensitive three-way email spam filtering
Bing Zhou, Yiyu Yao, Jigang Luo
2013· article· en· Journal of Intelligent Information Systems· Computer Science
distilled prediction:candidate · noneconsensus · none
193
citations
affunlabeled
Email classification with co-training
Svetlana Kiritchenko, Stan Matwin
2011· article· en· Computer Science
distilled prediction:candidate · noneconsensus · none
191
citations
affno abstractunlabeled
Detecting Malicious URLs Using Lexical Analysis
Mohammad Saiful Islam Mamun, Muhammad Ahmad Rathore, Arash Habibi Lashkari, Natalia Stakhanova, Ali A. Ghorbani
2016· book-chapter· en· Lecture notes in computer science· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
187
citations
afffundunlabeled
Battling the internet water army
Cheng Chen, Kui Wu, Venkatesh Srinivasan, Xudong Zhang
2013· article· en· Computer Science
distilled prediction:candidate · insufficient_payloadconsensus · none
184
citations
venueno affunlabeled
Academic Search Engine Optimization (<scp>ASEO</scp>)
Béla Gipp, Erik Wilde
2009· article· en· Journal of Scholarly Publishing· Computer Science
distilled prediction:candidate · scholarly_communication+research_integrityconsensus · scholarly_communication
166
citations
affunlabeled
Spam and the ongoing battle for the inbox
Joshua Goodman, Gordon V. Cormack, David Heckerman
2007· article· en· Communications of the ACM· Computer Science
distilled prediction:candidate · open_scienceconsensus · none
164
citations
affunlabeled
Spam filtering for short messages
Gordon V. Cormack, José María Gómez Hidalgo, Enrique Puertas
2007· article· en· Computer Science
distilled prediction:candidate · noneconsensus · none
138
citations
affunlabeled
Online supervised spam filter evaluation
Gordon V. Cormack, Thomas R. Lynam
2007· article· en· ACM Transactions on Information Systems· Computer Science
distilled prediction:candidate · noneconsensus · none
117
citations
afffundno abstractunlabeled
A Three-Way Decision Approach to Email Spam Filtering
Bing Zhou, Yiyu Yao, Jigang Luo
2010· book-chapter· en· Lecture notes in computer science· Computer Science
distilled prediction:candidate · metaepi_narrow+scholarly_communicationconsensus · none
111
citations
fundno affno abstractunlabeled
A deep learning model for Twitter spam detection
Zulfikar Alom, Barbara Carminati, Elena Ferrari
2020· article· en· Online Social Networks and Media· Computer Science
distilled prediction:candidate · noneconsensus · none
110
citations
affunlabeled
Detecting visually similar Web pages
Teh-Chung Chen, Scott Dick, James Miller
2010· article· en· ACM Transactions on Internet Technology· Computer Science
distilled prediction:candidate · noneconsensus · none
98
citations
fundno affunlabeled
SMSAssassin
Kuldeep Singh Yadav, Ponnurangam Kumaraguru, Atul Goyal, Ashish Gupta, Vinayak Naik
2011· article· en· Computer Science
distilled prediction:candidate · noneconsensus · none
94
citations
fundno affno abstractunlabeled
The Dimensions of Reputation in Electronic Markets
Anindya Ghose, Panagiotis G. Ipeirotis, Arun Sundararajan
2009· article· en· SSRN Electronic Journal· Computer Science
distilled prediction:candidate · noneconsensus · none
87
citations
afffundno abstractunlabeled
App store mining is not enough for app improvement
Maleknaz Nayebi, Henry Cho, Guenther Ruhe
2018· article· en· Empirical Software Engineering· Computer Science
distilled prediction:candidate · noneconsensus · none
84
citations
affunlabeled
Uncovering Susceptibility Risk to Online Deception in Aging
Natalie C. Ebner, Donovan M. Ellis, Tian Lin, Harold A. Rocha, Huizi Yang, Sandeep Dommaraju +5 more
2018· article· en· The Journals of Gerontology Series B· Computer Science
distilled prediction:candidate · noneconsensus · none
80
citations
affunlabeled
What Mobile Ads Know About Mobile Users
Sooel Son, Daehyeok Kim, Vitaly Shmatikov
2016· article· en· Computer Science
distilled prediction:candidate · insufficient_payloadconsensus · none
78
citations
affunlabeled
Spam Review Detection Using Deep Learning
G. M. Shahariar, Swapnil Biswas, Faiza Omar, Faisal Muhammad Shah, Samiha Binte Hassan
2019· article· en· Computer Science
distilled prediction:candidate · noneconsensus · none
77
citations
affno abstractunlabeled
Email mining: tasks, common techniques, and tools
Guanting Tang, Jian Pei, Wo-Shun Luk
2013· article· en· Knowledge and Information Systems· Computer Science
distilled prediction:candidate · scholarly_communicationconsensus · none
73
citations
affunlabeled
Purchase intention in an electronic commerce environment
Mahmud Akhter Shareef, Yogesh K. Dwivedi, Vinod Kumar, Gareth Davies, Nripendra P. Rana, Abdullah M. Baabdullah
2018· article· en· Information Technology and People· Computer Science
distilled prediction:candidate · noneconsensus · none
65
citations
affno abstractunlabeled
Developing an Immunity to Spam
Terri Oda, Tony White
2003· book-chapter· en· Lecture notes in computer science· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
64
citations

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