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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.

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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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Consumer Attitudes and Food Labeling
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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.

1,275 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.
1,275 works in the cohort · of 4,299,418page 26 of 26

Labels cover 3 of 1,275 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 1,275 of 1,275 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
ca statement for f1 visa sample pdf
2024· other· en· Zenodo (CERN European Organization for Nuclear Research)· Medicine
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
afffundunlabeled
Association between the diagnosis of diet-related non-communicable diseases and the use of nutritional labeling among Mexican, Mexican American, and non-Mexican American adults: a cross-sectional study from the International Food Policy Study 2021–2022
Isabel García-Perfecto, Alejandra Contreras‐Manzano, Kathia Larissa Quevedo, Christine M. White, Lana Vanderlee, Rachel Davis +7 more
2025· article· en· BMC Public Health· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractgemma · no categorygpt · no categorymodels agree
Kann “Food Addiction“ den Erfolg gewichtsreduzierender Behandlungen bei Übergewicht und Adipositas vorhersagen? Eine systematische Übersichtsarbeit und Metaanalyse mit Fokus auf Aspekten der Diversität
Georg Halbeisen, Marie Pahlenkemper, Luisa Sabel, Carol Richardson, Zaida Agüera, Fernando Fernández‐Aranda +1 more
2025· article· de· Adipositas - Ursachen Folgeerkrankungen Therapie· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
fundno affunlabeled
Adverse effects of vaping and electronic cigarettes
Álvaro García-Tenorio García, Jorge Jiménez Fernández, María Martínez Caparrós, Ainoa Martínez Ramos, Santiago Martos Quirós, Ramón Mengíbar Merino
2023· article· en· Tobacco Prevention & Cessation· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
venueno affunlabeled
What Else Does the Box Say?
Lynn Nagle
2008· article· en· Nursing leadership· Medicine
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
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