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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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Mobile Crowdsensing and Crowdsourcing
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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
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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.

449 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.
449 works in the cohort · of 4,299,418page 7 of 9

Labels cover 3 of 449 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 449 of 449 works in this cohort. Predictions are machine_predicted_unvalidated teacher distillation outputs. Candidate is the union; consensus is the intersection.

afffundno abstractunlabeled
Recruitment algorithms for vehicular sensor networks
Fabio Campioni, Salimur Choudhury, Usman Tariq, Ali Kashif Bashir
2020· article· en· Computer Communications· Computer Science
distilled prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Crowdsourcing Information Systems
Amin Ranj Bar, Muthucumaru Maheswaran
2013· book-chapter· en· SpringerBriefs in applied sciences and technology· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
2
citations
affunlabeled
Acquiring Reliable Ratings from the Crowd
Beatrice Valeri, Shady Elbassuoni, Sihem Amer-Yahia
2015· article· en· Proceedings of the AAAI Conference on Human Computation and Crowdsourcing· Computer Science
distilled prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Spatial organization to facilitate action
Grayden J. F. Solman, Alan Kingstone
2019· article· en· PLoS ONE· Computer Science
distilled prediction:candidate · insufficient_payloadconsensus · none
1
citations
venueno affunlabeled
Crowdsourcing and Stochastic Modeling
Srinivas Chakravarthy, Serife Ozkar
2016· article· en· Business and Management Research· Computer Science
distilled prediction:candidate · noneconsensus · none
1
citations
affunlabeled
MobileCrowdSensing (MCS)
Umang Mehta, Parth Soni, Jinan Fiaidhi
2020· preprint· en· Computer Science
distilled prediction:candidate · metaepi_narrow+scholarly_communicationconsensus · none
1
citations
venueno affunlabeled
Research on Mission Pricing of Crowdsourcing APP
Shaoyan Jiang, Songhao Lin, Yiquan Chen, Yuanbiao Zhang
2018· article· en· International Business Research· Computer Science
distilled prediction:candidate · noneconsensus · none
1
citations
affvenueunlabeled
Musées et médias sociaux
Sheila Carey
2015· article· fr· Documentation et bibliothèques· Computer Science
distilled prediction:candidate · metaepi_narrow+scholarly_communicationconsensus · scholarly_communication
1
citations
affunlabeled
Locally Differentially Private Truth Discovery Over Data Streams
Pengfei Zhang, Zhikun Zhang, Yang Cao, Shaowei Wang, Xiang Cheng, Zhang Ji
2025· article· IEEE Transactions on Mobile Computing· Computer Science
distilled prediction:candidate · metaepi_narrow+sts+scholarly_communicationconsensus · none
1
citations
affno abstractunlabeled
Crowdsourcing Database Systems and Optimization
Guoliang Li, Jiannan Wang, Yudian Zheng, Ju Fan, Michael J. Franklin
2018· book-chapter· en· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
1
citations

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