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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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Horticultural and Viticultural Research
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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.

1,041 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.
1,041 works in the cohort · of 4,299,418page 19 of 21

Labels cover 1 of 1,041 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 1,041 of 1,041 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
Proceedings. Ohio Grape-Wine Short Course, 1982
R. M. Kottman, Gail R. Nonnecke, G. A. Cahoon, A. M. Adams, Thomas J. Zabadal, Thomas J. Bürr +9 more
2013· article· en· The Knowledge Bank (The Ohio State University)· Agricultural and Biological Sciences
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
aboutno affunlabeled
Climate Change Impacts on Viticulture in Canada
Massimiliano Nicola Lippa, Eugenio Straffelini, Paolo Tarolli
2025· preprint· en· Agricultural and Biological Sciences
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Description of the used predictors.
2023· article· en· Figshare· Agricultural and Biological Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
ITK Vigne, Decision support tool for viticulture
Marc Gelly, Aline Bsaibes, Anaïs Guaus, Éric Lebon, Eric Jallas
2013· preprint· en· HAL (Le Centre pour la Communication Scientifique Directe)· Agricultural and Biological Sciences
machine prediction:candidate · noneconsensus · none
0
citations
fundno affunlabeled
A Wild Sheep Chase Through an Orchard
Jordan Dempsey, Leo van Iersel, Mark M. Jones, Yukihiro Murakami, Norbert Zeh
2024· preprint· en· arXiv (Cornell University)· Agricultural and Biological Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Building a more predictive model of terroir for the Anthropocene
E. M. Wolkovich, Christophe Rouleau‐Desrochers, Iñaki García de Cortázar Atauri, M. Andrew Walker, Thierry Lacombe
2025· article· en· Plants People Planet· Agricultural and Biological Sciences
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About