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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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Proceedings of the AAAI Conference on Human Computation 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.

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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.

14 results · 1 filter active ·
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20132024
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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.
14 works in the cohort · of 4,299,418page 1 of 1

Labels cover 0 of 14 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 14 of 14 works in this cohort. Predictions are machine_predicted_unvalidated teacher distillation outputs. Candidate is the union; consensus is the intersection.

fundno affunlabeled
Eliciting and Learning with Soft Labels from Every Annotator
Katherine M. Collins, Umang Bhatt, Adrian Weller
2022· article· en· Proceedings of the AAAI Conference on Human Computation and Crowdsourcing· Computer Science
distilled prediction:candidate · stsconsensus · none
26
citations
afffundunlabeled
Deja Vu: Characterizing Worker Reliability Using Task Consistency
Alex C. Williams, Joslin Goh, Charlie Willis, Aaron M. Ellison, James H. Brusuelas, Charles C. Davis +1 more
2017· article· en· Proceedings of the AAAI Conference on Human Computation and Crowdsourcing· Computer Science
distilled prediction:candidate · sts+scholarly_communicationconsensus · none
15
citations
afffundunlabeled
Lessons from an Online Massive Genomics Computer Game
Akash Singh, Faizy Ahsan, Mathieu Blanchette, Jérôme Waldispühl
2017· article· en· Proceedings of the AAAI Conference on Human Computation and Crowdsourcing· Computer Science
distilled prediction:candidate · sts+scholarly_communicationconsensus · none
12
citations
affunlabeled
Drafty: Enlisting Users To Be Editors Who Maintain Structured Data
Shaun Wallace, Lucy Van Kleunen, Marianne Aubin-Le Quere, Abraham Peterkin, Yirui Huang, Jeff Huang
2017· article· en· Proceedings of the AAAI Conference on Human Computation and Crowdsourcing· Computer Science
distilled prediction:candidate · metaepi_narrow+sts+scholarly_communicationconsensus · none
4
citations
affunlabeled
Reducing Error in Context-Sensitive Crowdsourced Tasks
Daniel Haas, Matthew Greenstein, Kainar Kamalov, Adam Marcus, Marek Olszewski, Marc Piette
2013· article· en· Proceedings of the AAAI Conference on Human Computation and Crowdsourcing· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
4
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

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