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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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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.

587 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.
587 works in the cohort · of 4,299,418page 9 of 12

Labels cover 1 of 587 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 587 of 587 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
Lazy Arithmetic Circuits.
Seyed Mehran Kazemi, David Poole
2016· article· en· National Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
afffundno abstractunlabeled
Order compression schemes
Malte Darnstädt, Thorsten Kiss, Hans Ulrich Simon, Sandra Zilles
2015· article· en· Theoretical Computer Science· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Learning chordal extensions
Defeng Liu, Andrea Lodi, Mathieu Tanneau
2021· preprint· en· Journal of Global Optimization· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Point Placement in an Inexact Model with Applications
Kishore Kumar V. Kannan, Pijus Kumar Sarker, Amangeldy Turdaliev, Asish Mukhopadhyay
2016· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Attention for Inference Compilation
William T. Harvey, Andreas Munk, Atılım Güneş Baydin, Alexander Bergholm, Frank Wood
2022· preprint· en· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Some Properties of Consensus-Based Classification
Vitaliy Tayanov, Adam Krzyżak, Ching Y. Suen
2017· book-chapter· en· Advances in intelligent systems and computing· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
venueno affno abstractunlabeled
The alpha-reliable shortest path problem.
Francesca Guerriero, Patrizia Beraldi
2008· article· en· Algorithmic operations research· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Randomized Gradient Boosting Machine
Haihao Lu, Rahul Mazumder
2018· preprint· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
A Bayesian concept learning approach to crowdsourcing
Paolo Viappiani, Sandra Zilles, Howard J. Hamilton, Craig Boutilier
2011· article· en· VBN Forskningsportal (Aalborg Universitet)· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
fundno affunlabeled
Thompson sampling for improved exploration in GFlowNets
Jarrid Rector-Brooks, Kanika Madan, Moksh Jain, Maksym Korablyov, Chenghao Liu, Sarath Chandar +2 more
2023· preprint· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Selective Sampling for Classification
François Laviolette, Mario Marchand, Sara Shanian
2008· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
SDI, Selective Dissemination of Information
Ling Liu, M. TAMER ÖZSU
2009· article· en· Encyclopedia of Database Systems· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
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