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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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Breastfeeding Practices and Influences
Retraction
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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.

2,046 results · 1 filter active ·
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20002025
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Machine labels · sparse coverage
Evidence
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
2,046 works in the cohort · of 4,299,418page 29 of 41

Labels cover 9 of 2,046 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 2,046 of 2,046 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
A experiência do puerpério para as famílias: revisão integrativa
Fernanda Rios da Silva, Maria Ribeiro Lacerda, Ingrid Meireles Gomes, Adelita Gonzalez Martinez Denipote, Luciana Midori Teruya
2021· article· pt· Research Society and Development· Medicine
machine prediction:candidate · noneconsensus · none
1
citations
aboutno affunlabeled
Breastfeeding practices
2019· dataset· en· Statistics Canada Dissemination· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Authors’ Note
Tasnim Nathoo, Aleck Ostry
2009· book-chapter· en· Medicine
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affno abstractunlabeled
Where Are We Going Wrong?
Sharanjit Kaur, Julie Smith-Fehr, Jana Stockham, Angela Bowen
2017· article· en· Clinical Lactation· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
ALEITAMENTO MATERNO: DOS POVOS PRIMITIVOS ATÉ A ATUALIDADE
Lindynês Amorim de Almeida, Ana Mirelle dos Santos, Maria Caroline de Melo Silva, Milena Alícia da Silva Santos, Ana Maria Souza de Melo, Ana Carolina Santana Vieira
2023· book-chapter· pt· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
BENEFÍCIOS DO ALEITAMENTO MATERNO PARA A MÃE E O BEBÊ
Barbara Vitória dos Santos Torres, Caroline Magna de Oliveira Costa, Diane Fernandes dos Santos, Jayane Omena de Oliveira, Jislene dos Santos Silva, Juliana Barbosa Valdevino de Oliveira +1 more
2023· book-chapter· pt· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
fundvenueno affunlabeled
[no title]
Marjolaine Héon, Patrick Martin
2013· article· fr· Aporia· Medicine
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affunlabeled
Uvula
John H. Duffus, Michael Schwenk, Douglas M. Templeton
2017· dataset· en· IUPAC Standards Online· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
venueno affno abstractunlabeled
10.1016/j.ynpm.2015.07.002
2000· article· en· Time to knit· Medicine
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
affunlabeled
Under WHO’s direction
Caroline Hyde Price
2001· article· en· Nursing Standard· Medicine
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About