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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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Innovation in Digital Healthcare Systems
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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.

70 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.
70 works in the cohort · of 4,299,418page 1 of 2

Labels cover 0 of 70 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 70 of 70 works in this cohort. Predictions are machine_predicted_unvalidated teacher distillation outputs. Candidate is the union; consensus is the intersection.

affno abstractunlabeled
Recent Trends in Digital Convergence Information System
Kyungyong Chung, Raouf Boutaba, Salim Hariri
2014· article· en· Wireless Personal Communications· Health Professions
distilled prediction:candidate · insufficient_payloadconsensus · none
24
citations
affno abstractunlabeled
Special Section on Mobile Content Delivery Networks
Pin‐Han Ho, Mingfu Li, Hsiang‐Fu Yu, Xiaohong Jiang, György Dán
2017· article· en· Computer Communications· Health Professions
distilled prediction:candidate · stsconsensus · none
1
citations
aboutno affunlabeled
전자의무기록과 원격의료에 대한 법적 고찰
2001· dissertation· ko· YUHSpace (Yonsei University Medical Library)· Health Professions
distilled prediction:candidate · metaepi_narrow+sts+research_integrity+insufficient_payloadconsensus · metaepi_narrow+research_integrity+insufficient_payload
0
citations
affunlabeled
Recommendation an Emergency Patient Destinations by LightGBM
Ryota Kawaguchi, Masao Kubo, Hiroshi Sato, Daizoh Saitoh, Takao Oya
2022· article· en· Proceedings of International Conference on Artificial Life and Robotics· Health Professions
distilled prediction:candidate · insufficient_payloadconsensus · none
0
citations
aboutno affunlabeled
Data Protection Laws on Publicly Available Data
Kyung Sin Park
2018· article· en· Institute for Legal Studies Chonnam National University· Health Professions
distilled prediction:candidate · sts+insufficient_payloadconsensus · none
0
citations
affunlabeled
Research on Mental Health Prediction using ML
Ms. Jaishree Wankhede, Shriram Phatale, Pranjay Dharkar, Rohan Ramteke, Ishika Rawlani, Dimple Mathare
2025· article· en· International Journal for Research in Applied Science and Engineering Technology· Health Professions
distilled prediction:candidate · stsconsensus · none
0
citations
affno abstractunlabeled
Special Issue: Interactive Multimedia Convergence
Jong‐Hun Kim, Raouf Boutaba, Junseok Yoo
2015· article· en· Multimedia Tools and Applications· Health Professions
distilled prediction:candidate · insufficient_payloadconsensus · none
0
citations
affno abstractunlabeled
Development of a home security Robot using Deep Learning and IoT
Shravan Aruljothi, Sharad Dewanand Parate, S Harshit, Nehal Dinesh Andani
2021· article· en· International journal of advance research, ideas and innovations in technology· Health Professions
distilled prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
A Study on Metis Self-Government
2012· other· ja· Health Professions
distilled prediction:candidate · metaepi_narrow+research_integrity+insufficient_payloadconsensus · insufficient_payload
0
citations

How this was built: Screen · Findings · About