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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.

affaffiliation
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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.

2,372 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.
2,372 works in the cohort · of 4,299,418page 30 of 48

Labels cover 1 of 2,372 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 2,372 of 2,372 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.

affunlabeled
Continuous Attractor Neural Networks
Thomas Trappenberg
2011· book-chapter· en· IGI Global eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
BJT and FET Models
Stephan J. G. Gift, Brent Maundy
2021· book-chapter· en· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
fundno affno abstractunlabeled
2 - Discharge time series data
Christine Leclerc
2020· dataset· en· HydroShare Resources· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Hardware design issues of fuzzy neural networks
A.F. Gobi, Witold Pedrycz
2004· article· en· IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04.· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
The Discrete Fourier Transform
D. Sundararajan
2024· book-chapter· en· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Generalised Pattern Search
Charles Audet, Warren Hare
2017· book-chapter· en· Springer series in operations research/Springer series in operations research and financial engineering· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Pyschoacoustic Feature Based Perceptual Segmentation
Chad R. Befus, Chris Sanden, John Z. Zhang
2010· article· en· The Journal of the Abraham Lincoln Association· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Differential Entropy and Gaussian Channels
Fady Alajaji, Po‐Ning Chen
2018· book-chapter· en· Springer undergraduate texts in mathematics and technology· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations

How this was built: Screen · Findings · About