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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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Software Testing and Debugging Techniques
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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.

996 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.
996 works in the cohort · of 4,299,418page 18 of 20

Labels cover 2 of 996 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 996 of 996 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.

affno abstractunlabeled
Erratum to: Testing of Communicating Systems
Hasan Ural, Robert L. Probert, Gregor von Bochmann
2017· erratum· en· IFIP advances in information and communication technology· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Impact of Automation on the Test Insertion
C. S. Karthik, H Naveen, Rajiv Gopal
2019· article· en· International Journal of Scientific Research in Computer Science Engineering and Information Technology· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
venueno affno abstractunlabeled
10.1016/b978-0-08-057206-2.50019-x
2000· book-chapter· en· Time to knit· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
affno abstractunlabeled
Trichomorphology
2025· book-chapter· en· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
TorchProbe: Fuzzing Dynamic Deep Learning Compilers
Qidong Su, Chuqin Geng, Gennady Pekhimenko, Xujie Si
2023· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Model Acceptance Testing
Nilesh Modi, Sorrell Grogan, Babak Badrzadeh, Jean Bélanger, Genevieve Lietz, S. Dennetière +1 more
2024· book-chapter· en· CIGRE green books· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Model-Based Testing of Non-deterministic Systems
Alexander Onofrei, Marc Frappier, Émilie Bernard
2025· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Automated Testing with Unity
Julia Naomi Rosenfield Boeira
2023· book-chapter· en· Apress eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Extended TTCN in software testing
W.B. Liu, P. Dasiewicz
2002· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
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How this was built: Screen · Findings · About