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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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Computational Physics and Python Applications
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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
venuejournal
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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.

1,060 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.
1,060 works in the cohort · of 4,299,418page 15 of 22

Labels cover 4 of 1,060 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 1,060 of 1,060 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.

affvenueunlabeled
Towering Fractals
David J. Jeffrey, Robert M. Corless
2023· article· en· Maple Transactions· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Using AI for Radio (Big) Data
Caroline Heneka, Julia Niebling, Hongming Tang, V. Balakrishnan, Jakob Gawlikowski, Gregor Kasieczka +2 more
2024· book-chapter· en· Astrophysics and space science library· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Introduction
William W. Hsieh
2023· book-chapter· en· Cambridge University Press eBooks· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
affunlabeled
Idiomatic Python
Alex Gezerlis
2023· book-chapter· en· Cambridge University Press eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
TDF_vector_map.cpg
Kayla Stan, Arturo Sánchez‐Azofeifa
2019· dataset· en· Harvard Dataverse· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affunlabeled
map.xml
David Topps, Michelle Cullen
2019· dataset· en· Harvard Dataverse· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affunlabeled
Merging of Machine Learning and Physics
William W. Hsieh
2023· book-chapter· en· Cambridge University Press eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Hydro One
2006· article· en· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
aboutno affunlabeled
Community Partners:
2003· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
ISCB/SPRINGER series in computational biology
Andreas Dress, Michal Linial, Olga G. Troyanskaya, Martin Vingron
2013· article· en· Bioinformatics· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affno abstractunlabeled
Partial Envelope Tensor Response Regression
Wenxing Guo, N. Balakrishnan, Shanshan Qin
2023· article· en· SSRN Electronic Journal· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
map_question_validation.xml
Michelle Cullen, David Topps
2019· dataset· en· Harvard Dataverse· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affunlabeled
map_node.xml
Michelle Cullen, David Topps
2019· dataset· en· Harvard Dataverse· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affunlabeled
map_node_counter.xml
Michelle Cullen, David Topps
2019· dataset· en· Harvard Dataverse· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations

How this was built: Screen · Findings · About