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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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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.

26 results · 1 filter active ·
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20032023
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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.
26 works in the cohort · of 4,299,418page 1 of 1

Labels cover 0 of 26 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 26 of 26 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
Query Expansion with Long-Span Collocates
Olga Vechtomova, Stephen Robertson, Susan Jones
2003· article· en· Information Retrieval· Computer Science
machine prediction:candidate · noneconsensus · none
53
citations
afffundno abstractunlabeled
Shallow pooling for sparse labels
Negar Arabzadeh, Alexandra Vtyurina, Xinyi Yan, Charles L. A. Clarke
2022· article· en· Information Retrieval· Computer Science
machine prediction:candidate · noneconsensus · none
42
citations
fundno affno abstractunlabeled
Efficient distributed selective search
Yubin Kim, Jamie Callan, J. Shane Culpepper, Alistair Moffat
2016· article· en· Information Retrieval· Computer Science
machine prediction:candidate · noneconsensus · none
24
citations
affno abstractunlabeled
Increasing evaluation sensitivity to diversity
Peter B. Golbus, Javed A. Aslam, Charles L. A. Clarke
2013· article· en· Information Retrieval· Computer Science
machine prediction:candidate · noneconsensus · none
22
citations
affno abstractunlabeled
Robust keyword search in large attributed graphs
Spencer Bryson, Heidar Davoudi, Lukasz Golab, Mehdi Kargar, Yuliya Lytvyn, Piotr Mierzejewski +2 more
2020· article· en· Information Retrieval· Physics and Astronomy
machine prediction:candidate · noneconsensus · none
20
citations
affno abstractunlabeled
Enhancing click models with mouse movement information
Zeyang Liu, Jiaxin Mao, Chao Wang, Qingyao Ai, Yiqun Liu, Jian‐Yun Nie
2017· article· en· Information Retrieval· Computer Science
machine prediction:candidate · noneconsensus · none
15
citations
affno abstractunlabeled
Latent word context model for information retrieval
Bernard Brosseau-Villeneuve, Jian‐Yun Nie, Noriko Kando
2013· article· en· Information Retrieval· Computer Science
machine prediction:candidate · noneconsensus · none
8
citations
affno abstractunlabeled
Swapping documents and terms
Charles L. A. Clarke, Gordon V. Cormack, Thomas R. Lynam, Chris Buckley, Donna Harman
2009· article· en· Information Retrieval· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
fundno affunlabeled
Neural ranking models for document retrieval
Mohamed Trabelsi, Zhiyu Chen, Brian D. Davison, Jeff Heflin
2021· preprint· en· Information Retrieval· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
Constructing click models for search users
Yiqun Liu, Jian‐Yun Nie, Yi Chang
2017· article· en· Information Retrieval· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
afffundunlabeled
(CF)2 architecture: contextual collaborative filtering
Dennis Bachmann, Katarina Grolinger, Hany F. ElYamany, Wilson A. Higashino, Miriam A. M. Capretz, Majid Fekri +1 more
2018· article· en· Information Retrieval· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations

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