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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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Multi-Criteria Decision Making
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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.

1,053 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.
1,053 works in the cohort · of 4,299,418page 6 of 22

Labels cover 3 of 1,053 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 1,053 of 1,053 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.

affno abstractunlabeled
Minimum cost consensus model with altruistic preference
Yingying Liang, Yanbing Ju, Yan Tu, Witold Pedrycz, Luis Martı́nez
2023· article· en· Computers & Industrial Engineering· Decision Sciences
machine prediction:candidate · noneconsensus · none
25
citations
fundno affunlabeled
Fuzzy AHP-based supplier selection in e-procurement
Morad Benyoucef, Mustafa S. Canbolat
2007· article· en· International Journal of Services and Operations Management· Decision Sciences
machine prediction:candidate · noneconsensus · none
24
citations
fundno affno abstractunlabeled
A fuzzy AHP model for assessing the condition of metro stations
Κωνσταντίνος Κεπαπτσόγλου, Matthew G. Karlaftis, Jason Gkountis
2013· article· en· KSCE Journal of Civil Engineering· Decision Sciences
machine prediction:candidate · noneconsensus · none
24
citations
affunlabeled
GROUP-BASED FAILURE EFFECTS ANALYSIS
Kouroush Jenab, B.S. Dhillon
2005· article· en· International Journal of Reliability Quality and Safety Engineering· Decision Sciences
machine prediction:candidate · noneconsensus · none
23
citations
affno abstractunlabeled
Fault tree analysis based on TOPSIS and triangular fuzzy number
Hongping Wang, Xi Lu, Yuxian Du, Chenwei Zhang, Rehan Sadiq, Yong Deng
2014· article· en· International Journal of Systems Assurance Engineering and Management· Decision Sciences
machine prediction:candidate · noneconsensus · none
22
citations

How this was built: Screen · Findings · About