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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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Big Data Technologies and Applications
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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.

167 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.
167 works in the cohort · of 4,299,418page 3 of 4

Labels cover 4 of 167 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 167 of 167 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.

affunlabeled
DJIN model of aging synthetic dataset
Spencer Farrell, Arnold Mitnitski, Kenneth Rockwood, Andrew D. Rutenberg
2021· dataset· en· Figshare· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
DJIN model of aging synthetic dataset
Spencer Farrell, Arnold Mitnitski, Kenneth Rockwood, Andrew D. Rutenberg
2021· dataset· en· Zenodo (CERN European Organization for Nuclear Research)· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affaboutunlabeled
A path to Big Data readiness
Claire C. Austin
2021· article· en· SSRN Electronic Journal· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractgpt · no categorygrok · no categoryopus · no categorymodels agree
Big data's first election victory
V. Venkatraman
2020· article· en· The New Scientist· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Open disaster risk reduction data platform
Joost van Ulden, W Chow, Drew Rotheram-Clarke, D Ulmi, A Fok, Tiegan Hobbs
2022· report· en· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Session details: Big data
M. TAMER ÖZSU
2012· article· en· Decision Sciences
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
affno abstractunlabeled
Big Data and Theory
Wolfgang Maaß, Jeffrey Parsons, Sandeep Purao, Alirio Rosales, Veda C. Storey, Carson Woo
2022· book-chapter· en· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
EmoGoals
2021· dataset· en· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Big Data
2018· book-chapter· en· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Big Data Schooling
2018· article· en· Zenodo (CERN European Organization for Nuclear Research)· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Data, Governance and Narrative
Scott Bennett
2025· book· en· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Big data en gerelateerde begrippen gedefinieerd
John Steenbruggen, Euro Beinat, Peter Nijkamp, Mark R. Opmeer, JM Jan Smits, van der Frank Kroon
2015· article· nl· TU/e Research Portal (Eindhoven University of Technology)· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
PHEME dataset of rumours and non-rumours
Arkaitz Zubiaga, Geraldine Wong Sak Hoi, Maria Liakata, Rob Procter
2016· dataset· en· Figshare· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affvenueno abstractunlabeled
The problem with data
Lonnie Aarssenb
2015· article· en· Ideas in Ecology and Evolution· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Deliverable 1.8 Data Management Plan V2
Torill Hamre, Hanne Sagen, Stein Sandven, Finn Danielsen, Geir Ottersen, Agnieszka Beszczyńska-Möller +4 more
2021· report· en· Zenodo (CERN European Organization for Nuclear Research)· Decision Sciences
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations

How this was built: Screen · Findings · About