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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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Machine Learning and Algorithms
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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
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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
Evidence
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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 4 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 teacher distillation outputs. Candidate is the union; consensus is the intersection.

affno abstractunlabeled
FSM Inference from Long Traces
2018· book-chapter· en· Lecture notes in computer science· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
13
citations
affunlabeled
Data sparseness in linear SVM
2015· article· en· International Conference on Artificial Intelligence· Computer Science
distilled prediction:candidate · insufficient_payloadconsensus · none
13
citations
afffundunlabeled
Inferring Symbolic Automata
2023· article· en· Logical Methods in Computer Science· Computer Science
distilled prediction:candidate · noneconsensus · none
10
citations
affunlabeled
The complexity of learning acyclic CP-nets
2016· article· en· International Joint Conference on Artificial Intelligence· Computer Science
distilled prediction:candidate · noneconsensus · none
10
citations
affunlabeled
Asterisk
2020· article· en· ACM/IMS Transactions on Data Science· Computer Science
distilled prediction:candidate · open_scienceconsensus · none
10
citations
afffundunlabeled
Finding Nearly Optimal GDT Scores
2011· article· en· Journal of Computational Biology· Computer Science
distilled prediction:candidate · noneconsensus · none
10
citations
afffundno abstractunlabeled
Mind Change Efficient Learning
2005· book-chapter· en· Lecture notes in computer science· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
9
citations
fundno affunlabeled
Model processing tools in UML
2005· article· en· Proceedings of the 23rd International Conference on Software Engineering. ICSE 2001· Computer Science
distilled prediction:candidate · noneconsensus · none
9
citations
affno abstractunlabeled
Active Learning with c-Certainty
2012· book-chapter· en· Lecture notes in computer science· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
9
citations
affno abstractunlabeled
Fundamentals of Machine Learning
2019· book-chapter· en· Computer Science
distilled prediction:candidate · insufficient_payloadconsensus · insufficient_payload
9
citations
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