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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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Empirical Software Engineering
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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.

371 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.
371 works in the cohort · of 4,299,418page 1 of 8

Labels cover 1 of 371 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 371 of 371 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.

afffundno abstractunlabeled
A field study of API learning obstacles
Martin P. Robillard, Robert DeLine
2010· article· en· Empirical Software Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
352
citations
affno abstractunlabeled
Curating GitHub for engineered software projects
Nuthan Munaiah, Steven Kroh, Craig Cabrey, Meiyappan Nagappan
2017· article· en· Empirical Software Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
345
citations
affno abstractunlabeled
Do developers update their library dependencies?
Raula Gaikovina Kula, Daniel M. Germán, Ali Ouni, Takashi Ishio, Katsuro Inoue
2017· article· en· Empirical Software Engineering· Computer Science
machine prediction:candidate · metaresearchconsensus · none
335
citations
afffundno abstractunlabeled
Naming the pain in requirements engineering
Stefan Wagner, Marcos Kalinowski, Michael Felderer, Priscilla Mafra, Antonio Vetrò, Tayana Conte +16 more
2016· article· en· Empirical Software Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
271
citations
affno abstractunlabeled
An in-depth study of the promises and perils of mining GitHub
Eirini Kalliamvakou, Georgios Gousios, Kelly Blincoe, Leif Singer, Daniel M. Germán, Daniela Damian
2015· article· en· Empirical Software Engineering· Computer Science
machine prediction:candidate · metaresearchconsensus · none
270
citations
afffundunlabeled
Pandemic programming
Paul Ralph, Sebastian Baltes, Gianisa Adisaputri, Richard Torkar, Vladimir Kovalenko, Marcos Kalinowski +11 more
2020· article· en· Empirical Software Engineering· Health Professions
machine prediction:candidate · insufficient_payloadconsensus · none
234
citations
affno abstractunlabeled
Bug characteristics in open source software
Lin Tan, Chen Liu, LI Zhen-min, Xuanhui Wang, Yuanyuan Zhou, ChengXiang Zhai
2013· article· en· Empirical Software Engineering· Computer Science
machine prediction:candidate · metaresearchconsensus · none
232
citations
affno abstractunlabeled
What do developers search for on the web?
Xin Xia, Lingfeng Bao, David Lo, Pavneet Singh Kochhar, Ahmed E. Hassan, Zhenchang Xing
2017· article· en· Empirical Software Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
177
citations
affno abstractunlabeled
Static test case prioritization using topic models
Stephen W. Thomas, Hadi Hemmati, Ahmed E. Hassan, Dorothea Blostein
2012· article· en· Empirical Software Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
156
citations
affno abstractunlabeled
Studying re-opened bugs in open source software
Emad Shihab, Akinori Ihara, Yasutaka Kamei, Walid M. Ibrahim, Masao Ohira, Bram Adams +2 more
2012· article· en· Empirical Software Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
138
citations
affno abstractunlabeled
Review participation in modern code review
Patanamon Thongtanunam, Shane McIntosh, Ahmed E. Hassan, Hajimu Iida
2016· article· en· Empirical Software Engineering· Computer Science
machine prediction:candidate · metaresearchconsensus · none
116
citations
affno abstractunlabeled
Studying software logging using topic models
Heng Li, Tse-Hsun Chen, Weiyi Shang, Ahmed E. Hassan
2018· article· en· Empirical Software Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
95
citations
affno abstractunlabeled
Configuring latent Dirichlet allocation based feature location
Lauren R. Biggers, Cecylia Bocovich, Riley Capshaw, Brian P. Eddy, Letha H. Etzkorn, Nicholas A. Kraft
2012· article· en· Empirical Software Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
92
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
machine prediction:candidate · noneconsensus · none
84
citations
affno abstractunlabeled
Towards just-in-time suggestions for log changes
Heng Li, Weiyi Shang, Ying Zou, Ahmed E. Hassan
2016· article· en· Empirical Software Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
79
citations

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