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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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Proceedings of the 31st ACM International Conference on Information & Knowledge Management
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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
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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.

13 results · 1 filter active ·
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20222022
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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.
13 works in the cohort · of 4,299,418page 1 of 1

Labels cover 0 of 13 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 13 of 13 works in this cohort. Predictions are machine_predicted_unvalidated teacher distillation outputs. Candidate is the union; consensus is the intersection.

affunlabeled
Sampling Enclosing Subgraphs for Link Prediction
Paul Louis, Shweta Ann Jacob, Amirali Salehi‐Abari
2022· article· en· Proceedings of the 31st ACM International Conference on Information & Knowledge Management· Computer Science
distilled prediction:candidate · noneconsensus · none
25
citations
affunlabeled
Effective Neural Team Formation via Negative Samples
Arman Dashti, Saeed Samet, Hossein Fani
2022· article· en· Proceedings of the 31st ACM International Conference on Information & Knowledge Management· Psychology
distilled prediction:candidate · insufficient_payloadconsensus · none
15
citations
affunlabeled
OpeNTF: A Benchmark Library for Neural Team Formation
Arman Dashti, Karan Saxena, Dhwani Patel, Hossein Fani
2022· article· en· Proceedings of the 31st ACM International Conference on Information & Knowledge Management· Computer Science
distilled prediction:candidate · noneconsensus · none
10
citations
affunlabeled
Early Stage Sparse Retrieval with Entity Linking
Dahlia Shehata, Negar Arabzadeh, Charles L. A. Clarke
2022· article· en· Proceedings of the 31st ACM International Conference on Information & Knowledge Management· Computer Science
distilled prediction:candidate · noneconsensus · none
8
citations
affunlabeled
BLUTune: Query-informed Multi-stage IBM Db2 Tuning via ML
Connor Henderson, Spencer Bryson, Vincent Corvinelli, Parke Godfrey, Piotr Mierzejewski, Jaroslaw Szlichta +1 more
2022· article· en· Proceedings of the 31st ACM International Conference on Information & Knowledge Management· Computer Science
distilled prediction:candidate · noneconsensus · none
5
citations
afffundunlabeled
Risk-Aware Bid Optimization for Online Display Advertisement
Rui Fan, Erick Delage
2022· article· en· Proceedings of the 31st ACM International Conference on Information & Knowledge Management· Decision Sciences
distilled prediction:candidate · insufficient_payloadconsensus · none
3
citations
affunlabeled
SEERa: A Framework for Community Prediction
Soroush Ziaeinejad, Saeed Samet, Hossein Fani
2022· article· en· Proceedings of the 31st ACM International Conference on Information & Knowledge Management· Physics and Astronomy
distilled prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Named Entity-based Question-Answering Pair Generator
Aritra Kumar Lahiri, Qinmin Hu
2022· article· en· Proceedings of the 31st ACM International Conference on Information & Knowledge Management· Computer Science
distilled prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About