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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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demographic modeling and climate adaptation
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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
aboutaboutness

The four routes compose: require the funder route and exclude affiliation to get the funder-only stratum no affiliation-based frame ever sees.

684 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.
684 works in the cohort · of 4,299,418page 2 of 14

Labels cover 0 of 684 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 684 of 684 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
EVA London 2021 - Index
Jonathan Weinel, Jonathan P. Bowen, Ann Borda, Graham Diprose
2021· article· en· Electronic workshops in computing· Decision Sciences
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
18
citations
affvenueaboutunlabeled
Population Change in Canada
Anthony C. Masi
2017· article· en· Canadian Studies in Population· Decision Sciences
machine prediction:candidate · noneconsensus · none
17
citations
aboutno affunlabeled
Analyzing US Census Data
Kyle E. Walker
2023· book· en· Decision Sciences
machine prediction:candidate · noneconsensus · none
16
citations
affunlabeled
The Long-Run Effects of Cesarean Sections
Ana Costa-Ramón, Mika Kortelainen, Ana Rodríguez‐González, Lauri Sääksvuori
2020· article· en· The Journal of Human Resources· Decision Sciences
machine prediction:candidate · noneconsensus · none
12
citations
affvenueaboutunlabeled
The OncoSim-Breast Cancer Microsimulation Model
Jean Hai Ein Yong, Claude Nadeau, W. Michael Flanagan, Andrew J. Coldman, Keiko Asakawa, Rochelle Garner +3 more
2022· article· en· Current Oncology· Decision Sciences
machine prediction:candidate · noneconsensus · none
12
citations
affunlabeled
Analysing differences between scenarios
David F. Hendry, Felix Pretis
2022· article· en· International Journal of Forecasting· Decision Sciences
machine prediction:candidate · noneconsensus · none
11
citations

How this was built: Screen · Findings · About