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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
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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
Evidence
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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 3 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. 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
Sentiment Crawling
Joseph S. Mei, Richard Frank
2015· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
25
citations
affunlabeled
Fighting the spam wars
Paweł Gburzyński, Jacek Maitan
2004· article· en· ACM Transactions on Internet Technology· Computer Science
machine prediction:candidate · noneconsensus · none
23
citations
affunlabeled
Usability meets access control
Konstantin Beznosov, Philip Inglesant, Jorge Lobo, Rob Reeder, Mary Ellen Zurko
2009· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
23
citations
affno abstractunlabeled
Fraudulent and malicious sites on the web
Ahmed Obied, Reda Alhajj
2007· article· en· Applied Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
20
citations
affunlabeled
Spam Zombies from Outer Space
John Aycock, Nathan Friess
2006· article· en· PRISM (University of Calgary)· Computer Science
machine prediction:candidate · noneconsensus · none
18
citations
affunlabeled
Online gaming cheating and security issue
Ying‐Chieh Chen, Jing-Jang Hwang, Ronggong Song, George Yee, Larry Korba
2005· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
18
citations
affno abstractunlabeled
Information Asymmetry and Thwarting Spam
Thede Loder, Marshall Van Alstyne, Rick Wash
2004· article· en· SSRN Electronic Journal· Computer Science
machine prediction:candidate · noneconsensus · none
17
citations
afffundunlabeled
OpenIDemail enabled browser
San-Tsai Sun, Kirstie Hawkey, Konstantin Beznosov
2010· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
14
citations
affunlabeled
Prevalence and classification of web page defects
Ejike Ofuonye, Patricia Beatty, Scott Dick, James Miller
2010· article· en· Online Information Review· Computer Science
machine prediction:candidate · noneconsensus · none
14
citations
affno abstractunlabeled
Topic-Association Mining for User Interest Detection
Anil Kumar Trikha, Fattane Zarrinkalam, Ebrahim Bagheri
2018· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
14
citations
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