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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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Infrastructure Maintenance and Monitoring
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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.

1,403 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.
1,403 works in the cohort · of 4,299,418page 28 of 29

Labels cover 1 of 1,403 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 1,403 of 1,403 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.

aboutno affunlabeled
Victim of the Wind
2009· other· en· OhioLink ETD Center (Ohio Library and Information Network)· Engineering
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Index
PEng John Olusegun Ogundare
2018· paratext· en· Engineering
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
aboutno affunlabeled
The Issue of Inspection
Roy Parker
2010· book-chapter· en· Policy Press eBooks· Engineering
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Smart Roads Classification
2021· article· en· RiuNet (Politechnical University of Valencia)· Engineering
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Comparison of Rail Deterioration Prediction Models
Rajendran Bharath Rajendir, Rebecca Dziedzic
2024· book-chapter· en· Lecture notes in civil engineering· Engineering
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Sources of Information in Highways: A Bibliography
Daniel Krummes, Betty Ambler, Paul L. Atwood, Janet Bix, Laurel Clark, John Gallwey +6 more
2001· article· en· eScholarship (California Digital Library)· Engineering
machine prediction:candidate · bibliometricsconsensus · none
0
citations

How this was built: Screen · Findings · About