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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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Forecasting Techniques and 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.

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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.

497 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.
497 works in the cohort · of 4,299,418page 5 of 10

Labels cover 0 of 497 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 497 of 497 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
Timeline Analysis
Xiaodong Lin
2018· book-chapter· en· Decision Sciences
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Introduction
Maxime C. Cohen, Paul-Emile Gras, Arthur Pentecoste, Renyu Zhang
2022· book-chapter· en· Springer series in supply chain management· Decision Sciences
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
1
citations
venueno affunlabeled
Weighted Analytics – What Do the Numbers Suggest?
Craig E. Peterson, Vinodh Chellamuthu, Joseph Lovell
2022· article· en· Journal of Emerging Sport Studies· Decision Sciences
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Modeling and forecasting
Lawrence A. Boland
2014· book-chapter· en· Cambridge University Press eBooks· Decision Sciences
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Perceiving prospects properly
Jakub Steiner, Colin Stewart
2016· preprint· en· Zurich Open Repository and Archive (University of Zurich)· Decision Sciences
machine prediction:candidate · noneconsensus · none
1
citations

How this was built: Screen · Findings · About