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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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Data Stream Mining Techniques
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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.

322 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.
322 works in the cohort · of 4,299,418page 4 of 7

Labels cover 0 of 322 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 322 of 322 works in this cohort. Predictions are machine_predicted_unvalidated teacher distillation outputs. Candidate is the union; consensus is the intersection.

afffundno abstractunlabeled
Supporting Smart Interactions with Predictive Analytics
Patrick Martin, Marie Matheson, Jimmy Lo, Joanna Ng, Daisy Tan, Brian Thomson
2010· book-chapter· en· Lecture notes in computer science· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
4
citations
venueno affunlabeled
A New Natural Language Processing–Inspired Methodology (Detection, Initial Characterization, and Semantic Characterization) to Investigate Temporal Shifts (Drifts) in Health Care Data: Quantitative Study
Bruno Barbosa Miranda de Paiva, Marcos André Gonçalves, Leonardo Rocha, Milena Soriano Marcolino, Maíra Viana Rego Souza-Silva, Jussara M. Almeida +32 more
2024· article· en· JMIR Medical Informatics· Computer Science
distilled prediction:candidate · noneconsensus · none
4
citations
affunlabeled
Incremental Feature Learning Using Constructive Neural Networks
Armin Sadreddin, Samira Sadaoui
2021· article· en· 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI)· Computer Science
distilled prediction:candidate · metaepi_narrow+scholarly_communicationconsensus · none
3
citations
affunlabeled
On the Feasibility of Forgetting in Data Streams
A. Pavan, Sourav Chakraborty, N. V. Vinodchandran, Kuldeep S. Meel
2024· article· en· Proceedings of the ACM on Management of Data· Computer Science
distilled prediction:candidate · open_scienceconsensus · open_science
3
citations
affunlabeled
Quilt: Robust Data Segment Selection against Concept Drifts
Minsu Kim, Seong-Hyeon Hwang, Steven Euijong Whang
2024· article· en· Proceedings of the AAAI Conference on Artificial Intelligence· Computer Science
distilled prediction:candidate · open_scienceconsensus · none
2
citations
affunlabeled
nipy/nipype: 1.2.2
2019· other· en· Zenodo (CERN European Organization for Nuclear Research)· Computer Science
distilled prediction:candidate · metaepi_narrow+scholarly_communication+insufficient_payloadconsensus · insufficient_payload
2
citations
affunlabeled
Mining Data - Streams
Hanady M. Abdulsalam, David B. Skillicorn, Pat Martin
2008· book-chapter· en· IGI Global eBooks· Computer Science
distilled prediction:candidate · metaepi_narrow+open_scienceconsensus · none
2
citations
affunlabeled
Mining hidden constrained streams in practice: Informed search in dynamic filter spaces
Νικόλαος Παναγιώτου, Ioannis Katakis, Dimitrios Gunopulos, Vana Kalogeraki, Elizabeth Daly, Jia Yuan Yu +1 more
2016· article· en· 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
2
citations
affunlabeled
DragStream: An Anomaly And Concept Drift Detector In Univariate Data Streams
Anne Marthe Sophie Ngo Bibinbe, Abdoul Jalil Djiberou Mahamadou, Michael Franklin Mbouopda, Engelbert Mephu Nguifo
2022· article· en· 2022 IEEE International Conference on Data Mining Workshops (ICDMW)· Computer Science
distilled prediction:candidate · metaepi_narrow+open_scienceconsensus · open_science
2
citations
affno abstractunlabeled
Improving Machine Learning Performance Using Conceptual Modeling.
Arturo Castellanos, Alfred Castillo, Monica Chiarini Tremblay, Roman Lukyanenko, Jeffrey Parsons, Veda C. Storey
2021· article· en· AAAI Spring Symposium Combining Machine Learning with Knowledge Engineering· Computer Science
distilled prediction:candidate · metaepi_narrow+research_integrityconsensus · none
2
citations
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